Implementation of precision agriculture should be continuously accelerated to increase agricultural productivity and prevent degradation of soil, land, and water resources and also environmental pollution. For this reason, soil loss due to erosion must be well-known and identified carefully followed by efforts to reduce the amount of soil erosion in all areas that is greater than permissible erosion. To achieve these efforts, application of appropriate technology is needed. The application of drones is very helpful in the preparation and provision of precision agricultural management plans. In addition to being very helpful in estimating the amount of erosion, the use of drones is also very useful for setting recommendations for alternative soil and water conservation measures needed for each land unit. From the results of research conducted in Karyasari Village, Leuwiliang District, Bogor Regency, using drones to estimate erosion by USLE (Universal Soil Loss Equation) method is very helpful in determining the value of plant factors (C), soil management (P), slope (L) and slope length (S), and predicting actual soil erosion in each Land Management Unit (LMU). For the LMU whose actual soil erosion estimates are greater than tolerable soil erosion, the use of drones is very helpful in finding steps to compose recommendation of soil conservation measures for each Land Management Unit. The use of drones is very useful to support and assist precision agricultural management, including many aspects of planning, operational and monitoring and evaluation levels.

As a tropical country, Indonesia has two seasons, where each season (rainy or dry seasons) lasts for 6 months. In Indonesia, the beginning of rice planting time occurs at the beginning of the rainy season, but a high diversity of rainfall causes the beginning of rice planting to become varies in some areas. In the area that have a same rainfall, however, the beginning of planting time can be also vary due to the rotation of irrigation water distribution. Seeing this phenomenon, the beginning of planting time is very important to be identified, since it can be used to estimate when the harvest time comes and also the production.The purpose of this study were to identify the growth phase of rice crops based on reflectance and vegetation index using Sentinel-2A imageries and distribution of growth phase of rice crops. The research method begins with a transformation of digital numbers into reflectance values in order to correct the effect of a light source, sensor, atmospheric. Analysis of reflectance (NIR) and vegetation index (NDVI, SAVI, EVI) values were carried out on 56 samples spread over five phases of rice growth, i.e. wet fallow, vegetative, generative, harvesting, dry fallow. The results showed that the NDVI can distinguish five phases of rice growth more clearly than EVI, SAVI, NIR. Pattern of reflectance value and vegetation index in the five phases of rice growth shows a quadratic model. Overall accuracy of the composite temporal of NDVI imageries is small (34,30%). The rice crops in harvesting phase is largest (42,04%)

Rice crops is a crucial commodity for Indonesian people. Most Indonesian people consume rice as a staple food. With the rate of population growth that continues to increase, the demand for rice also increases. Rice crops Monitoring needed in ensuring national food availability. This paper presents a new method for predicting the growth phase of rice crops with a rice field area approach and identifying patterns of vegetation index from Sentinel 2A satellite image data series from May to September 2017. The study was conducted in Sukamekar Village, Karawang District, West Java, Indonesia from June to August 2017. Polynomial regression models used to identify the relationship between the growth phase of rice crops and vegetation index. The vegetation index used is NDVI. In determining the vegetation index in the rice field two methods are obtained, first calculating the average value of the vegetation index on the pixels in each rice fields area. Second, by removing the pixels that contact with the border of each rice field area, then calculate the average value of the vegetation index on the pixel of the rice field area. From both of methods, an algorithm was developed to get the rule base to determine the phase of rice growth based on the value of the vegetation index in each field area. The model developed was implemented in the same location in January 2019 using Sentinel-2A Image. Based on field validation in February 2019, the accuracy of the first method was 70% while the second method was 75%.

This study developed a method of satellite imagery-based land-cover/land-use mapping for Indonesia at 1:50,000 scale, but with a very detailed level of categorization. The method was developed by taking into account: (a) categorization target specified in the reference classification scheme, (b) the ecological characteristics of the Indonesian region, (c) the type of data used, and (d) the main approach that can be applied to all regions in Indonesia. A landscape ecological approach was selected, by combining digital and visual interpretation. The main data source was Landsat-8 OLI recorded in various dates of recording between 2016 and 2018, supported with SPOT-7 and Sentinel 2A imagery. Digital analysis includes geometric and radiometric corrections, pan-sharpening, and vegetation index transformation. Visual interpretation was carried out with an interpretive overlays strategy and/or land unit approach. Field work was carried out for collecting information on terrain characteristics that are relevant to the land-cover/land-use variation, to be used as a basis for re-interpretation process. Based on the developed methods, a set of land-cover/land-use maps on a scale of 1: 50,000 of the southern part of Sumatera, except Lampung Province, was delivered. It covers 247 map sheets. The interpretation accuracies have been assessed statistically, and they reached 79.54% for Bengkulu, 80.75% for Jambi, 79.2% for Riau Islands, and 81.02% for Bangka-Belitung. With a large number of classes has been mapped, i.e. over 70 categories, the accuracy levels achieved in this study were considerably high. Some notes on the results of the mapping were also included in this report.

Development of satellite sensor systems capable of producing high spatial resolution digital images has led to the emergence of various alternative methods beside the more established per-pixel multispectral classifications. One alternative method is object-based image analysis (OBIA). At the beginning of its development, OBIA was primarily used for high spatial resolution images. However, the OBIA is now widely applied to images with medium- and even low-spatial resolutions. This study aimed to compare the effects of the OBIA and per-pixel classifications using using Landsat-8 OLI medium-spatial resolution image. Since the per-pixel classification relies solely on spectral aspects on various spectral bands, while the OBIA classification made use of spatial aspects as the main criteria, this study also made use of two land-cover/land-use classification schemes as references, i.e. spectral-oriented and spatial-oriented classification systems. The spectral-oriented classification scheme specifies categories from spectral perspective, i.e. pixel values in n-dimensional feature space; while the spatial-oriented one specifies categories with respect to their spatial characteristics. By using Kulon Progo region as a test area, the results showed that the OBIA classification was able to provide higher accuracy than that of per-pixel classification, both by referring to the spectral and spatial dimension classification schemes. The increase in accuracy provided by the OBIA classification proved to be greater when applied with a spatial dimension classification scheme, which was more than 10%, as compared to the improvement obtained by the spectral dimension classification scheme, i.e. 7%. This study also recommends the need for comparison studies using higher-spatial resolution imagery.

The decentralization concept right after the reformation era indeed has created partial watershed management. Whereas watershed management must have the principle of "one watershed, one management." Citarum Watershed problem has occurred in Cirasea sub-watershed as part of the upstream area because it has a very bad erosion index due to agriculture activity. Citarum problem is unfinished even though the government has issued enough budget for building physics. Therefore, a collaboration between stakeholders for strengthening whose role is needed after technical activities are carried out. Areas that have the status of protected forests or conservation forests, steep topography, large areas of critical land, and elevations above 1000 m generally have many institutions involved in managing sub watershed from government programs and initiatives from communities that have a highly interesting in the environment. Economic motivation influenced by the function of the river, meaning that community participate ion will be high if the river has a positive function for the community. Stakeholder Analysis (SA) divides the role of the institution into four parts, the role of Subject, Players, Crowd, and Context Setters. Identifying the connectivity of institutions at the central and local levels can be used to arrange some recommendation as a strategy for Cirasea watershed institutional strengthening. The internal and external factors propertied by the Cirasea Watershed Institution based on the results of the Strength Weakness Opportunities and Threats (SWOT). The analysis resulted in priority recommendations, the certainty of coordination between programs and sectors in real terms, postharvest certainty to increase farmer’s motivation in cultivating coffee, and Players groups should be coordinate and strengthen the performance capacity of the Subject group to control Crowd group behavior.

Urban heat island is a condition when metropolitan area has warmer temperature that surrounding rural area. High population and activity inside the city can be the factors that trigger urban heat island. Indonesia has some large cities with big population. Jakarta and Surabaya are two largest and most populous cities in Indonesia. In this study, the effect of urban heat island in those two cities will be compared using Landsat 8 data in the period of 2018. The correlation between land surface temperature and the normalized difference vegetation index (NDVI) were analyzed to explore the impacts of the green areas on the urban heat island. The result showed the differences of surface temperature between two largest cities in Indonesia in 2018. The result also showed negative correlation between NDVI and surface temperature that indicates that the green area can decrease the effect on the urban heat island.

Cekungan Bandung Urban Region is a national strategic area from the point of economic interest. Rapid economic growth in the region has led to increased population growth and Built-up land use. Increased use of built up land in urban areas can cause urban sprawl which can have serious impacts that will damage the structure and function of urban area ecosystems, thus hampering the development of regional sustainability. Landscape Metrics can be used to analyze the urban form of the Cekungan Bandung Area in time series from 1983-2015 so that it can identify the changing trends. The results of the analysis show that during this period urban growth in the Cekungan Bandung Area showed a positive trend with a compact, connected, un-fragmented and near-square urban form. Being a concern of Patch Density (PD) and CONTIG values because it shows an increasing number of patches so that patches are more fragmented and the distance between the built up patches shows the trend is increasingly scattered on the edges of the Cekungan Bandung Area which can cause urban sprawl.

Jabodetabek and Greater Bandung areas experiencing the phenomenon of urban expansion which has caused both metropolitan areas to be more connected and become a mega-urban area by a corridor through a conurbation process. Karawang regency is part of the region in the Jakarta-Bandung mega-urban corridor. Vegetated lands in Karawang Regency are threatened by land conversion as a result of the Jakarta-Bandung conurbation process which tends to trigger the formation of the Jakarta-Bandung Mega-Urban Region. The increasing population in Karawang Regency has increasingly triggered the conversion of vegetated lands into built-up lands so that it can cause the region to be prone to anthropogenic disasters such as flood because of water catchment areas that reduced. The aims of this study are to analyze the land cover changes that occured in Karawang Regency in 2005–2015 and 2015–2025 and mapping the floodprone areas in Karawang Regency. Land cover changes analysis is carried out by overlapping Landsat Imagery of land cover in the 2005 and 2015 and 2015 and 2025 based on projection results using CA-Markov method. Mapping the floodprone areas is carried out by a Weighted Overlay process on variables or factors that are considered influential on the occurrence of floods. Karawang Regency has experienced an increase in land use change, especially changes in vegetated lands to built-up lands from year to year. Flood-prone areas analysis result in Karawang Regency show that most of the total area of Karawang Regency is classified as high and moderate classification flood-prone areas.

Geographic Object-Based Image Analysis (GEOBIA) is an emerging approach in remote sensing image analysis and classification which relies on segments or objects created by a group of pixels on the image. GEOBIA has been utilized for many remote sensing applications with various degree of success. However, from the literature, its application for landform analysis and classification is still rare. This study aims to test GEOBIA interpretation capabilities to identify landform in part of Opak Watershed (Central Java, Indonesia) using Landsat 8 OLI and DEMNAS imagery (30 and 8- meters pixel size, respectively) and evaluate the result. Both image data were fused to create an image with high spectral and spatial resolution and contains elevation data, as an input for the segmentation process. GEOBIA interpretation process was performed gradually; first, initial Multiresolution Segmentation Algorithm was conducted to identify the variation of slope found in the study site. Then, the slope segments/objects were used to identify landform using Ruleset-Based Classification considering the image object information including object values, pattern, shape, and other parameters. The accuracy of the result was evaluated based on the percentage accuracy of the landform classification. From this study, we found that fusion-image and GEOBIA are capable of distinguishing landform elements very well with the percentage of overall accuracy is 88%. This result shows that GEOBIA has potential in identifying and classifying landform objects.

The Bogor-Depok-Bekasi (Bodebek) region is a suburb of the Capital City of Jakarta located in West Java Province and part of the West Java development area (WP) 1, this region has experienced rapid regional development and economic growth due to the urbanization process in Jakarta. The development of this region was marked by the growth of Gross Regional Domestic Product (GRDP) of 42.08% and population increase of 19.54% during the period 2010-2016. However, developments in the Bodebek region have led to the formation of sprawl areas in several areas, leading to unsustainability and leading to the conversion of agricultural land. This study aims to 1) Identify and project Gross Domestic Regional Products (GDRP) growth and population based on a growth model approach using using simple regression equation; 2) Identify the spatial pattern of built-up areas in the Bodebek region by using the Moran index and the Local Indicator of Spatial Association (LISA); and 3) Identify development patterns in the Bodebek region using cluster analysis. The results of this study indicate that built-up areas in the Bodebek region are concentrated (clustered) in the core urban area of the Bodebek Region and its surroundings. In addition, population and GDRP growth in the Bodebek region follow the saturation growth model. Based on the result of cluster analysis, there are 3 clusters in the Bodebek region based on social, economic, population and land use data.

Situ Ciseupan is a natural pond which the areas surrounding it are bordering with conservation area (Mount Papandayan Nature Park). The area is expected to function as buffer zone, yet local communities utilize this area for agricultural activities, which plays important role in their economics. Such condition creates high risk of erosion that could lead to decline in soil fertility as well as water quality of the pond and nearby streams. This study aimed to estimate the rate of soil loss in Situ Ciseupan area under the current land-use/cover (LU/LC) patterns and hypothetical LU/LC patterns for exploring the impact of land-use/cover conversion on soil loss. Soil loss rate is estimated using the Revised Universal Soil Loss Equation (RUSLE) model. The study shows that average soil loss rate under the current LU/LC is 35.3 tons/ha/year. Areas classified as very high and severe erosion contributes 64.71% of the total soil loss. For simulating the impact of LU/LC conversion, we developed five scenarios in which the current LU/LC is converted into five hypothetical LU/LC types, each consists of a single type of LU/LC, i.e. bare soil, settlements, grassland, agriculture land, and forest. The simulation results show that if the current LU/LC is converted into 100 % of bare soil, settlement area, grassland, and agricultural field, the soil loss rate will increase by 1163%, 873%, 520%, and 317%, respectively. In contrast, the soil loss rate will decrease by 93%, if the current LU/LC is converted to 100% forest.

Landslide is caused by meteorological and geomorphological factors. Landslide is one of the most common disaster that occur in Indonesia. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 64 landslides in Purworejo. To reduce the impact of landslide, effective evacuation routes are needed. Determining of evacuation routes can be done in various methods, one of methods is use a spatio-cost approach. The purpose of this research is to determine the most effective evacuation routes to reduce the impact of landslide. Spatio-cost parameters obtained by certain paramaters. The parameters are physical parameters and some social parameters derived from the appearance on the surface of the earth, such as housing, number of population, land use, slope direction, roads and also the wide of the roads. These parameters are processed to look for evacuation routes using Least Cost Path (LCP) method. The expected result of this research is evacuation routes that can help people around disaster-prone areas to prepare. This on going research is important to improve disaster manajemen in Indonesia, especially for landslide in Bruno, Purworejo, Central Java.

Monitoring of rice field, as a place for producing rice is very important to realize one aspect of food security, namely food availability. Modern agriculture has been widely utilize remote sensing data, especially optical images for monitoring agricultural land in various aspects of land management. However, the use of optical images is hampered by cloud cover when monitoring rice fields because most of them located in tropical countries, so there is an alternative to using SAR imagery that has ability to penetrate clouds. One of the SAR image products is Sentinel-1A with band C on its sensors which was launched in 2014 and the data can be utilized by the wider community for free. The purpose of this study was to determine the ability of multitemporal Sentinel-1A SAR imagery in identifying paddy and non-paddy in Bantul Regency’s agriculture field which was measured through its mapping accuracy. Sentinel-1A multi-temporal images with ten recording dates from February to May 2018 were used as the main data for this study. The method used is a digital classification with two approaches i.e. parametric with MLC algorithm and non-parametric with k-NN algorithm. In addition, the Sentinel-1A, which consists of VV and VH polarization, performed in three classification schemes (VV multi-temporal, VH multi-temporal, and VV and VH multi-temporal). The classification results show that multi-temporal Sentinel-1A can be used to identify paddy and non-paddy fields with an accuracy of 77.69% (VV multitemporal-MLC), 82.15% (VH multi-temporal-MLC), 88.45% (VV and VH multi-temporal-MLC), 76.64% (VV multitemporal-kNN), 78.47% (VH multi-temporal-kNN) and 79.52% (VV and VH multi-temporal-kNN).

Accurate information of actual paddy field area is very importance for food security to support decision making. Remote sensing data is a use-full tools to detect and to create the paddy field area information, because of spatial and temporal characteristics. It is challenging in using the time series single polarization Synthetic Aperture Radar (SAR) data to detect such information. This research used the Sentinel-1 VH polarization and GRD level in Subang district of West Java in 2018 with 12-days temporal interval, around 30 temporal data were used. The remote sensing image pre-processing steps were applied in every single data such as geometric correction, backscattered calculation, topographic flattening, and lee filtering. The ready data was in 30x30 meter pixel resolution then be filtered by temporal filter using median moving window. Then, it was transformed using phenological approach by temporal transformation. The are several RGB composite products were compared and analyzed by using paddy field map from Ministry of Agriculture as reference data. The results show that the best RGB composite for detecting paddy field area is the RGB temporal combination of minimum backscatter Jan-June as Red layer, minimum backscatter July-December as Green layer, and maximum backscatter Jan-December as Blue layer. The blue color indicates the paddy field, it means that during Jan-June and July-Dec the area was inundated, and during a year there was vegetation covering the area.

Soybean is one of the national strategic commodities because of its role as an income and nutrition source for Indonesian people. Until now, the performance of soybean agribusiness is still far from expectations, as indicated by stagnant production and increasing import. One of the problems of the soybean production to achieve self-sufficiency is the unavailability of land allocation that is intended explicitly for planting soybean. This work aims to evaluate the soybean land suitability in Bogor District, West Java Province, Indonesia using the spatial decision tree algorithm. The proposed algorithm has been applied to a spatial dataset consisting of a target layer that represents soybean land suitability and seven explanatory layers that represent land characteristics of Bogor District. The result is a spatial decision tree that generated 26 rules with accuracy of 92.73% and the relief variable as the root node.

Robusta coffee is one of the focuses for realizing Desa Emas program in Mekarbuana Village, Tegalwaru Sub-District, Karawang Regency. The West Java Provincial Government choose Mekarbuana Village as one of the 20 target villages of Desa Emas (Enterpreneur, Mandiri, Adil and Sejahtera) in 2016. Robusta coffee production in Mekarbuana Village still fluctuates annually and certainly has an impact on the income of coffee farmers. A recommendation is needed in the direction of coffee development that considers various aspects of robusta coffee in the context of developing coffee commodities. The purpose of this study is to analyze the land suitability of Robusta coffee based on measurable soil chemical properties and provide recommendations for the direction of the robusta coffee development in an effort to realize the Desa Emas program. The land suitability analysis measured by taking disturbed soil samples to analyzed for the chemical properties of the soil and then conducted a land suitability analysis. Recommendations for the direction of the robusta coffee development are carried out by identifying strengths, weaknesses, opportunities and threats for the development of robusta coffee using SWOT analysis based on the results of land suitability analysis and interview result with coffee farmers in Mekarbuana Village. The results of the land suitability analysis indicate that actual land suitability has limiting factors, they are pH, slope, organic C levels, and water availability. Based on the SWOT analysis, increasing the quantity and quality of robusta coffee production in Mekarbuana Village requires various improvements in land input and maintenance.

Rice is one of the main food crops, not only in Indonesia but also in Asia and worldwide. Global rice production and consumption involve more than 250 million farmers and 3.3 billion consumers, respectively. The most common pest that frequently attacks rice is brown planthopper or BPH (Nilaparvata lugens Stal). The objective of this study was to estimate area infested by BPH by using satellite spectral data analysis. The methods consisted of five stages, i.e. data preparation, field checking, determination of planting dates, Vegetation Index (VI) analysis, and estimation of infested area. Data preparation included data downloading and projecting, image cropping and digitizing. Field checking was carried out to validate the data and to get historical data of BPH infestation. The planting dates were determined by investigating the annual pattern of VI and rice plant development. VI analysis was using NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Estimation of infested area consisted of 4 procedures i.e. data normalization, Vegetation Index Unit (VIU) calculation, infested area estimation, and comparison of NDVI-EVI value. This study indicated that satellite spectral data analysis could be used to estimate the area infested by BPH. It has been shown that the analysis could differentiate between healthy and infested area. The VI value and the peak of infested area were lower and earlier, respectively compared to the healthy plantation. NDVI analysis was more effective compared to NDVI analysis in estimating BPH infested area.

Providing accurate and up-to-date agricultural vegetation maps is a very important task for agricultural land evaluation and monitoring. These maps allow various kinds of spatial analyses could be conducted to optimally manage and utilize of land resources. One of the newly developed approaches in information extraction from remote sensing data is objectbased approach or widely known as Geographic Object-Based Image Analysis (GEOBIA). This study aims to utilize GEOBIA and a pan-sharpened WorldView-2 image (0.5 m pixel size) to identify and map agricultural vegetation types in part of Dieng Plateau, Central Java, Indonesia. A multiresolution segmentation algorithm was used to partition the image into vegetation object candidates based on some segmentation criteria. The accuracy of segments created were evaluated by visually comparing the segmentation result with the objects border on the image and field visit. A hierarchical conceptual model was created to systematically classify targeted agricultural vegetation objects, and the relevant interpretation keys for each object were identified. For the classification process we implemented a rule-based classification based on segment’s values, shape, homogeneity, texture, compactness, asymmetry, roundness, elliptic fit, number of pixel and border length. The result showed that the combination of GEOBIA and WorldView-2 were able to discriminate and map the types of agricultural vegetation into cabbage, carica, carrot, chili, potato, potato with soil solarization, and tamarillo with a reasonably high accuracy.

The estimation of precipitation using weather satellite is beneficial to measure global rainfall with high temporal and spatial resolution. The understanding of accuracies and limitations of precipitation satellite data is essential to study by verifying the satellite estimation with the observation data from rain gauges. The aim of this study is to verify the radar and rain gauges rainfall data in rainy season (December 2013 to March 2014), which consist three approaches namely point-to-grid, area-weighted and grid-to-grid for daily interval in Ciliwung watershed and point-to-grid for hourly interval in Darmaga and Pondok Betung stations. The verification methods consist of two namely continuous verification statistics and categorical verification statistics. The result of continuous verification statictic shows the highest correlation is point-to-grid and the variability of errors are from 0 to 45.76 according to magnitude of MAE. The radar rainfall are underestimated to observation with ME negatives. The categorical verification statistics shows that accuracies of those three approaches have the average of 0.44 and biases below 1.5. The correlations in hourly interval with point-to-grid approach in Pondok Betung and Darmaga are lower than daily interval, while the accuracies are higher. The application of remote sensing such as radar technology, satellite precipitation estimation feasible to detail rainfall data in a watershed, especially in locations which observation stations are not available.

Convectively coupled equatorial waves (CCEWs) are often identified by space-time filtering techniques via a fast-Fourier transformation (FFT) that make use of the eigenvalues (frequency and zonal wavenumber) derived from the linear shallow water theory. Here, instead, a method is presented for identifying CCEWs by using a combined FFT and empirical orthogonal function (EOF). We show that this technique is better at isolating CCEW’s signals from noises and undesirable spectral mixtures among the modes. In particular, using lag-regression analysis, the structures associated with each eigenvector signal resemble equatorial wave features consistent with a linear wave theory. The first eight EOFs of the Kelvin-filtered outgoing long-wave radiation (OLR) at the equator represent Kelvin waves with zonal wavenumbers 2, 3, 4, and 5, respectively. The horizontal structures of MRG (n=0) and ER (n=1) waves are well isolated by only the first two EOFs, while the higher EOF modes capture spectral mixtures among the wave modes. On the other hand, the first ten EOF modes of Tropical Depression (TD)-type-filtered OLR anomalies represent TD-type wave activities across different regions; where the first four modes indicate TD-type wave activity over the South East China, while the modes of 5-6 and 9-10 indicate the TD-type wave activity over Africa and Central America, respectively. This study highlights the importance of the combined space-time FFT-EOFs analysis to better capture the horizontal structures of CCEWs that occur across a range of spatial scales.

Accurate information of evapotranspiration (ET) will provide important information in irrigation systems. ET can be estimated using simple method using satellite data and field measurements. Field measurements was conducted in Air Buluh Village, Bangka Belitung Island on seven land cover (three different palm-oil ages, bare land, agricultural land, reclamation, shrub) by using the Penman-Monteith method. Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), spectral reflectance short-wave band (SWIR), were used from satellite data derived from Landsat 8-OLI/TIRS data. The analysis shows that the vegetation index has positive correlation with ET field measurements with a correlation only 12-15% and errors 1.3 mm/day, with SAVI has the lowest correlation, followed by EVI and NDVI. SWIR has negative correlation with 7% coefficient correlation. SWIR is a sensitive band with surface water content. SWIR increases with decrease of surface water content. Estimation of ET were built by using simple regression between ET and vegetation index and SWIR. Regression result shows that EVI has the highest correlation, 42% followed by NDVI and SAVI, 39%, and 30%. The equation formed by vegetation index and SWIR is: ET=EVI(2.68e^{6.14 SWIR}), ET=NDVI(2.67e ^4.57SWIR), ET=SAVI(4.45e^{4.07 SWIR}) ,with P-value < 0.05 for all method with error 1-2.5 mm/day

The decrease of land surface temperature (LST) in reclaimed mining site can be taken as indication of environmental quality improvement. Remote sensing technology is able to provide information on LST over a large area so that it can be used to assess the effect of land-cover characteristic of mining-affected landscape on LST. This research used Landsat 8 images to study the relationships between land-use and LST in a coal-mined landscape of a mining company in South Kalimantan. Specifically, this study aimed to examine the effect of different planting years of reclaimed forest on LST and to examine the effect of different dominant tree species of reclaimed forest on LST. Data source for LST calculation was band 10 of Landsat 8 thermal infrared sensor (TIRS) from recording years of 2014 and 2018. The images analysis consisted of the following steps: LST data extraction using Avdan and Jovanovska (2016) algorithm, land surface emissivity calculation following Valor and Caselles (1996), and overlay of LST maps with land-cover maps. A field work to measure stand characteristics of reclaimed forests was also conducted. The result shows that reclaimed forests with different planting years tends to have similar temperature-decreasing ability. This is shown by similar average LST values of reclaimed forest stands planted at year 1997, 2014, 2015, and 2016, which were around 27°C at year 2018. Meanwhile, reclaimed forest stands with different dominant trees species and total basal area also tend to similar temperature decreasing ability, being around 4.7-5.7°C after 3-4 years of planting. The results indicates that the tree species currently used by PT Adaro Indonesia for post-mining forest reclamation have similar ability in decreasing LST and the LST decrease could occur in a relatively short time.

A realistic realization of heavy rainfall in the mountainous region in Java is important for weather forecasts and climate simulations due to its potential disaster caused such as flashfloods and landslides. To study the relation between heavy rainfall events and such impacts, high resolution simulations are necessary to account for the appropriate representation of the geographical features and as well as the convection that typically occurred in such settings which lead to heavy rainfall events. The planetary boundary layer (PBL) scheme in a numerical model is a sensitive aspect which may lead to very different results of the rainfall simulation in a mountainous region setting. While such study of the PBL are still quite rare, in this study high-resolution Weather Research and Forecasting (WRF) simulations were carried out for different schemes of the PBL, i.e. Yonsei University (YSU) and Shin-Hong (SH) schemes, for a heavy rainfall event during 28 - 30 November 2018 over Central Java. The numerical model employs nesting with horizontal resolutions of 9 km, 3 km and 1 km for the outer, middle and inner domains, respectively. Some satellite observations, i.e. Global Precipitation Measurement (GPM), CPC Morphing Technique (CMORPH) Global Precipitation Analyses, and Global Satellite Mapping of Precipitation (GSMaP), were used to compare with the model results. The results showed that the heavy rainfall simulated with the SH scheme was closer to both the TRMM and CMORPH dataset than simulation result with the YSU scheme, although both schemes resulted in a similar spatial rainfall pattern. The findings suggest that the SH scheme can be used at a kilometer resolution to adequately replicate the scale and evolution of the observed rolls in heavy rainfall events in the mountainous area with steep slopes. This research is a part of the project A Blueprint for an Indonesian Landslide Early Warning System (BILEWS).

C-Band Doppler Weather Radar is one of the supporting instruments used in the meteorology for weather forecasting and analysis in Lombok region. One of the purpose of weather radar is estimates the rainfall rates in a wide range and long period, but in the process radar possess limitation called "radar limitation" causes data generated by the radar is inaccurate, this can be overcome by implementing quality control. The purpose of this study is to improve the estimation of rainfall using radar products with the step of Clutter Correction (PPI), Bright Band Echoes (MAX) and Z-attenuation (SRI) to be compared SRI products then convert to RIH and PAC that show the value of rainfall rates estimation with data from AWS/ARG in the Lombok area then verified with RMSE values and correlation values to represent the estimated amount of rainfall that can support operations using weather radar. The results of the research obtained published that quality control is very necessary in improving the quality of the results and the quality of the rain reported in the Lombok. Comparison of the results after quality control and non-quality control showed better results after quality control was performed.

Daily rainfall forecast has high importance in Indonesia because of its supporting role in various sectors. However, highresolution forecasts with many ensembles require high computing costs that hamper the development of regional weather/climate forecast, especially in Indonesia. This study aims to develop a daily operational ensemble forecasts with adequate validity and relatively low-computing cost to forecast rain occurrences over Indonesia. The model ensemble forecasts contain 21 ensemble members; each of them is obtained from Global Ensemble Forecast System (GEFS) data. To justify the forecast results, this study used Global Precipitation Measurement (GPM) satellite data as a comparison. The forecasting procedure is as follows: First, Global Forecast System data are used as the initial condition of WRF to obtain regional scale forecast. Second, the output from WRF is used to obtain the correction factor by simple delta method. Third, the correction factor is then used to downscale all ensemble members of GEFS data to regional scale. Finally, using all ensemble members, probability of rain occurrences is then calculated. The rain occurrences are divided into six categories: sunny (no rainfall), cloudy, light rainfall, medium rainfall, high rainfall, and very high rainfall. The processes mentioned above are automated, and the outputs are issued daily. Results show that the model forecasts are consistent with GPM satellite data and have adequate forecast skills, especially over land areas. Moreover, the forecasts have a relatively short running time of approximately 22 hours; without the need of supercomputers. These results feature the possibility of low-computing cost of model ensemble daily weather forecasts over Indonesia.

Jakarta area with high built-up areas and CO₂ emission cause urban heat island (UHI). The characteristics of UHI, such as temperature and wind must be known in order to overcome UHI problems. In this study, urban heat islands in Jakarta is simulated using Weather Research and Forecasting (WRF) model coupled with Noah-MP Land surface model. The aim of the study is to evaluate the WRF-urban modeling system’s performance. The method used is by conducting Building Effect Parameterization (BEP) scheme experiments in WRF simulation with high spatial resolution (1 km horizontal grid spacing) then compare to observation data during vernal equinox day (day without a shadow) on 14th – 16th March 2018 and 8th - 10th Oct 2018 over Jakarta metropolitan area. The results show that the correlation of near-surface air temperature and wind speed in March is better than in October, while the wind direction has a better correlation in October. Concerning the urban areas, Central and west Jakarta has the highest temperature area on March 15 th ,2018, with the highest temperature was 32.7°C at Kemayoran. Meanwhile, only west Jakarta area has the highest temperature (>32°C) on October 9th 2018, with the highest temperature was 32.16°C at Cengkareng.

Having the second-longest coastline in the world, Indonesia is highly vulnerable to coastal disasters and climate change impact. Kaliwlingi, a coastal village in Brebes, lies on the estuary of Pemali River in the north of Java Island, Indonesia. Disaster preparedness and climate change mitigation are one of the five aspects factored in coastal disaster resilience assessment at the village level. This quantitative research was designed to determine the coastal community resilience in Kaliwlingi by analyzing disaster preparedness and climate change mitigation. The variables were grouped into infrastructure and non-infrastructure. While the former consists of early warning systems and physical infrastructure, the latter includes government responsibility and regulations on disaster preparedness. The questionnaire items were encoded to the Likert scale, and then the data were analyzed to understand how the community perceived the prevailing disaster and climate change preparedness. The results showed that the community had substantial knowledge of emergency food stockpiles and proper recognition on shoreline structures and early warning system but poor comprehension of available shelter, emergency actions by the local government, and disaster preparedness. Based on these indicators, this community was concluded as moderately resilient. Also, gender seemed to be the strongest determinant of resilience index. Relative to the male community members, the female had a broader knowledge of disaster preparedness regulation and access to evacuation routes. Although livelihood significantly shaped the resilience index, formal education attainment appeared to have less influence.

Remote sensing based indices has been particularly widely used for natural hazard monitoring. Drought is one of natural hazard that mainly effects agricultural production in Indonesia. Therefore, the remote sensing based indices for drought monitoring have many variations of indexes to overcome the needs. In this research, new remote sensing based indices, namely Difference Drought Index (DDI) was used to monitor the water cover of vegetation that can be differentiate the drought for six categories, wet cover to very strong drought in El Nino strong year 2015. The other remote sensing based indices also being studied, VHI (Vegetation Health Index) to compare with DDI index. The results showed these two indexes performed good result in identifying drought in El Nino year 2015. For future studies, more indexes are needed to be examined in order to find better index used for monitor drought in Indonesia.

Indonesia is an archipelago with a geographical location prone to natural disasters such as volcanic eruptions and tsunamis. One way to cope with this disaster is by optimizing disaster sensor communications on earth through satellite technology. One of satellite technologies that can be used for disaster sensor communication is by utilizing the VHF Data Exchange System (VDES). VDES is a maritime communication system using Very High Frequency (VHF). In this study the authors conducted a study of VDES implementation for sensor communication in Indonesia. This study will analyze and provide conclusions about the capacity and capability of VDES as transciever data of all disaster sensor platforms in Indonesia. From this study, it was concluded that VDES can be used as an alternative technology to support disaster sensor communication in Indonesia.

Flood mapping is important to do as part of emergency response efforts in the event of a disaster. Usually, there are limitations in flood mapping with a direct approach and ineffective mapping efforts, such as: lack of time, cost, and number of personnel in the field. Utilization of remote sensing data can be used as an alternative to overcome this problem with an indirect approach. One of the uses of remote sensing data can be used to provide objective information and can be used to monitor, detect and map floods spatially-temporally. In this paper we will discuss of flood inundation mapping with several color composite combinations from dual-polarization Sentinel 1-A data using three classification methods. The data used is Sentinel 1-A dual-polarization data, in vertical-vertical receive (VV) and vertical-horizontal receive (VH) transmission mode. These datasets were acquired pre- (08 December 2016) and post- (22 February 2017) the flood even of 20 - 22 February 2017. This method is the detection of flooding of surface changes, the classification process for flood inundation mapping (maximum likelihood, minimum distance, and Mahalanobis distance), and then calculating an accuracy assessment. Six kinds of color combinations polarization combination Red Green Blue from the three classification methods. Flood detection of surface changes from a combination of six types of color composites gives very diverse results. The six types are VH_pre-flood - VH_post-flood - VH_pre-flood / VH_post-flood. VH_preflood - VV_post-flood - VH_pre-flood / VV_post-flood. VV_pre-flood - VV_post-flood - VV_pre-flood / VV_postflood. VV_pre-flood - G: VH_post-flood - VV_pre-flood / VH_post-flood. VV_pre-flood - VH_post-flood - VV_postflood. VH_pre-flood - VH_post-flood - VV_post-flood. From the calculation of accuracy for each classification method, the combination of VV_pre-flood - VH_post-flood - VV_post-flood using the minimum distance classification has the highest level of accuracy compared to other combinations. Therefore, to make a flood inundation map using dualpolarization dual-sentinel 1-A data, this composite is the best.

Palm oil industry delivers a large part of Indonesia’s economy due to the fact that is the major exporter in the list of producers worldwide. However, environmental protection and sustainability of the oil palm plantation is a global issue since its development was identified as a driving factor of deforestation as well as forest degradation in Indonesia. Therefore, the projection of oil palm development is required in order to support the national program of low-carbon development planning. The objective of this study is to develop projections of oil palm expansion for 2045 that comprise of a business-as-usual scenario due to considering the complex interactions between historic and present land use, socioeconomic conditions and biophysical constraints. An integrative spatial approach of the CLUE (Conversion of Land Use and its Effects) model was applied to explore land use changes for a scenario of further oil palm development in both Sumatra and Kalimantan, Indonesia. According to the BAU scenario, the total estimated area planted with oil palm plantation in 2045, both Sumatra and Kalimantan, had reached 13.8 Mha and 10.6 Mha, respectively. If we compare with the estimation result of the BAU with legal compliance, no-expansion allowed in forest area, oil palm plantation in both islands are: 11.2 Mha in Sumatra and 7.3 Mha in Kalimantan. Meanwhile, based on the zero-deforestation scenario, the expansion of oil palm is still possible in both islands, Sumatra and Kalimantan, for about 1,632,019 ha and 869,844 ha, respectively. The different trend of their changes per year for each island shown the different characteristics of each island triggered by biophysical environments, the historical development of land, as well as social-economic conditions.

Gunung Halimun Salak National Park (GHSNP) corridor is an area that connects Salak and Halimun Mountain, and has a role in animal movement, breeding and living. This study aims to characterize the vegetation structure in a restoration area in the corridor of Gunung Halimun Salak National Park. The vegetation characteristics was analyzed through structural vegetation datasets such as Canopy Height Model (CHM) and some vegetation indices namely; Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI). Significance of the approach was evaluated by the Mann Whitney test. The results indicated that the restoration area of HSNPC consist of seedlings, saplings, poles and trees. GHSNP’s corridor canopy layer consists of five canopy layers, namely strata A (> 30 m), B (20 – 30 m), C (4 – 20 m), D (1 – 4 m), and E (0 – 1 m). The most important species are Schima wallichii, Agathis dammara, Bellucia axinanthera and Macaranga triloba. The effective vegetation index to see the differences vegetation structure are NDVI and RVI vegetation index.

Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * ^'RED')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest.

Increased carbon dioxide in the atmosphere causes the surface temperature of the earth to warm up and has a major impact on climate change globally. Plants in the forest as the biggest absorber of carbon dioxide used in the process of photosynthesis, then the results are stored in the form of biomass in plant organ tissues. The purpose of the study was to estimate biomass and carbon storage in the Mount Tampomas Protected Forest Area in Sumedang, West Java. Mount Tampomas protected forest area is divided into areas dominated by pine plant species (Pinus merkusii) and mixed jungles. In the two regions the NDVI class was classified into 5 classes as the basis for calculating the stand density, biomass and carbon storage. The relationship between NDVI classes and stand densities can be demonstrated by linear and quadratic regression models. The quadratic regression model has r of 0.79 while the linear regression model of 0.78. Quadratic regression model is the best model to connect the NDVI class and stand density, where the NDVI class and stand density are very strongly related. The total biomass and carbon deposits sequentially in protected forest areas dominated by pine are 132,613.79 tons and 62,328.48 tons C, while the total biomass and carbon deposits sequentially in mixed forest protected areas are 64,682.95 tons and 30,400.99 tons C, so that the total biomass and carbon storage sequentially in the Mount Tampomas Protected Forest Area are 197,296.74 tons and 92,729.47 tons C.

Evapotranspiration (ET) is one of the climate elements which plays an important role in ecosystem water balance, including in oil palm plantations. Therefore, many mathematical equations and algorithms have been developed and designed to estimate and determine the spatial distribution of evapotranspiration. Remote sensing data are one of the important sources and techniques to estimate spatial variation of various climate elements, including ET. The main objective of this research is to estimate the spatial variation of ET using the SEBAL algorithm and Landsat-8 imagery of a large-scale commercial oil palm plantation, i.e. PT Perkebunan Nusantara VI Batanghari, Jambi Province, Indonesia. The analysis is carried out using Landsat-8 (OLI/TIRS) data and reference meteorological data from a micrometeorology flux tower. We calculated surface radiance, reflectance, albedo, Normalized Difference Vegetation Index (NDVI), emissivity, surface temperature, net radiation, soil heat flux, sensible heat flux, and latent heat flux to derive the hourly and daily evapotranspiration from the study area. Validation of ET from the SEBAL model were performed against ET from aerodynamic measurements from a micrometeorological tower at the same site. Differences in ET within “only oil palm cover” are relatively low and that difference in ET over the entire area of the oil palm plantation is mainly between oil palm vs. open lands, roads, and buildings. The evapotranspiration values of oil palm cover (NDVI 0.45-0.54) were between 2.42 ± 0.36 – 3.36 ± 0.17 mm d^-1 . There was no significant difference between ET derived from SEBAL compared to aerodynamic methods (p-value = 0.598; r = 0.75).

Depok is a city with rapid undergoing economic and infrastructure development in Indonesia. Such increasing growth in infrastructure affected positively to increase the population, and then, it threat an existence of remaining green open areas. Vegetation on green open areas has a role as the carbon storage in forms of trees. This research aim is to find the correlation between tree carbon stock and Leaf Area Index (LAI) in green open space. The method were used vegetation analysis and field measurement to collect diameter data for estimate carbon stock and hemispherical photography to measure the LAI. The result shown that the highest tree carbon stock were located in University of Indonesia City Forest (87.02 ton C/ha) with the highest vegetation index was Falcataria moluccana. The amount of tree carbon stock in Pancoran Mas Forest Park was 13.96 ton C/ha and in Lembah Gurame Park was 6.25 ton C/ha. LAI estimated in University of Indonesia City Forest was between 3.30 – 6.55, Pancoran Mas Forest Park 2.96 – 3.77 and Lembah Gurame Park 1.46 – 2.92. The correlation between the two variables were weak, r_xy=0.32 and has polynomial equation C = -2.0874LAI² + 10.188LAI - 9.5021 with R²= 0.477.

Remote sensing has been widely used in the estimation of forest aboveground biomass (AGB) which is essential for climate change mitigation,by using either optical or radar data and its combination. This estimation of AGB from remote sensing data is now supported by the availability of the freely available dual-polarization Sentinel 1 SAR data. However, the assessment of the accuracy for measuring AGB from the VV and VH polarization in Sentinel-1 data in Indonesia is still limited. This study aims to assess the performance of VV and VH polarization and the combination with texture data from Sentinel-1 for estimating AGB in tropical forest of Barru Regency, South Sulawesi. The AGB was calculated by using backscatter value from C-band SAR dual-polarization and Grey Level of Measure (GLCM) texture data from Sentinel-1 as the independent variables, and ground inventory plots as the dependent variable. Twenty-three plots of field inventory data were collected whereas 16 plots were used in the regression models and the remaining seven plots were used to validate the result. The allometric equation was used to calculate the biomass value of the field survey data then multilinear regression models were generated by using biomass value, backscatter data from VV and VH polarization, and texture data. The performance of the resulted multilinear regression models was compared by looking at the coefficient of determination (R²) and RMSE value using cross-validation. The results demonstrated that combination of VH and GLCM texture suggest as the best to estimate the AGB based on higher value of R² = 0.44 and SE 83.7 kg/tree. In conclusion, VH polarization usage in vegetation AGB modelling has been able to predict 3 % higher than by using VV polarisation. The inclusion of texture also had been able to increase the model performance by 5 to 7 % which demonstrated the importance of having texture variables in the analysis of AGB.

The national law in Indonesia stated that cities are required to have 30% of green open space from the total area. Tangerang City is one of the capital's buffer cities that has been continuously grown since the 1990s. The development of the city is quite rapid, and the emergence of various activities such as household activities, transportation, and industry are encouraging changes in green open space areas. Data from the regional statistic shows that open green space in Tangerang City is only 12.56% (2,319.21 hectares) of the total area. The less open green space area might degrade its main function as an absorber of emissions or pollutants, especially for carbon dioxide gas (CO₂). This study aims to estimate the biomass, total CO₂ stored by vegetation. This study uses direct measurements and vegetation index to formulate the ideal biomass formula. The pre-field activities begin with the extraction of NDVI (Normalized Differential Vegetation Index) and EVI (Enhanced Vegetation Index) from SPOT 7 imageries. The biomass allometric formulas used in this study are the Brown and Lugo equation, to find biomass values from tree stand parameters such as diameter breast height (DBH) and tree height. Quantitative and spatial analysis used in this study is a regression analysis of biomass and vegetation index value. The results show EVI has a better regression value and total biomass of around 26 million kilograms.

Urbanization has triggered an increasing of population rate and the contribution of gas emissions consequently due to of human activities is also increased. Green Open Space (GOS) is a balancer of an urban environment and able to create a microclimate. Research objective is to assess a carbon stock in urban trees according to regional typologies, characteristics and development in Depok City. In this study, the type of area was distinguished into typology of region based on three main criteria, namely: population density, income and road density. Sample of GOS has been selected visually using ARCGIS 10.5 software, then field observation was conducted to collect some supporting data through interview and questionnaire distribution. The results showed that the higher area’s type the value of carbon stock is increasing, and GOS for green belt is the biggest contributor of carbon stock with 5.37 tons / km². The community and government strongly support the movement of GOS development towards a Low Carbon City so that they need guidance and assistance from experts.

Gunung Halimun Salak National Park is a widest tropical rainforest conservation area that still exists in West Java and Banten Provinces. The park has been facing deforestation, forest degradation and forest fragmentation problem, on the other hand the area is a natural habitat of Javan Gibbon, so habitat restoration is important to maintain its connectivity. The aim of this study is to provide information to the park administration about the potential area which can be restored to facilitate Javan gibbon movement. The analyses were performed in Condatis, a modelling software for use in landscape planning to explore the connectivity of the wildlife corridor, based on species movement in response to climate change. Variable data prepared in the form of a raster is habitat data, Javanese gibbon habitat suitability data through Maxent application, data source and target layer. The bussines as usual (BAU) scenario and restoration scenario are created by illustrating changes in landscape connectivity by converting non-forest areas into forests. Corridor areas and paths can be indicated by high flow values from BAU scenario, while priority restoration areas are indicated by changes in flow values between scenarios. Priority restoration areas are divided into 3 classes: high, medium and low, high and medium classes are found in the Cikaniki Tea Plantation area, the Pasir Kerud forest block and Muara Tilu.

Information of habitat suitability and distribution of hornbill is useful for the conservation programs in Indonesia, however, the information availability is very limited. In order to fill the information gap, we have mapped the distribution and modeling of 13 hornbill habitat suitability in Indonesia based on presence data from the secondary. The data were analyzed using ArcGIS 10.5 and Maximum Entropy 3.4.1 software. Our results showed that the favorable habitat of hornbills was dense vegetation in the lowlands area. Meanwhile, the slope condition did not affect its distribution. Distribution of hornbills in Indonesia tends to be disturbed by human activities. Environmental variables that contributed significantly to the suitability habitat were normalized difference vegetation index (23.1%), distance from the edge forest (23.9%), and distance from the agricultural field (19.4%) with AUC 0.749±0.0245. Percentage of unsuitable habitat about 53.62% was higher than suitable habitat (46.38%). More detailed habitat classification showed that the medium and high habitat suitability classes were very limited (17.47%). We also found that the distribution of hornbills was more dominant in the unprotected areas (66.35%) compared to the protected areas (33.65%). The condition lead to the hornbill extinction, and therefore habitat conservation efforts are urgently needed.

In 2015 the Jambi Province had been suffered from a big fire which destroying about 115.634 ha or 23% of the total area. This paper describes the measurement of the magnitude and direction of forest and land cover changes, especially in the burnt area of Harapan Rainforest, Jambi, occurred in 2015. The main objective of this study is to find out the magnitude as well as the direction of forest cover changes caused by fire. The magnitude and direction of changes were derived by using Change Vector Analysis (CVA) Approach on the basis of medium-resolution of Landsat 8 acquired in 2014 representing before fire condition and Landsat 8 recorded in 2016 representing after fire condition. The study shows that the magnitude of changes in the study site varied widely starting from slightly to heavily changes. The identified direction of the changes could be grouped into positive dan negative directions, where the negative directions include degradation (D1) and deforestation (D2), while the positive directions include regrowth (D3) and revegetation (D4). The area suffering from degradation and deforestation are 3540 ha and 3106 ha respectively, while the area experiencing vegetation the vegetation growth and revegetation are 55 ha and 227 ha. The study concludes that the CVA could be used to find out the magnitude and change direction (positive and negative) effectively

The rising global temperatures due to climate change force animals to look for cooler and suitable areas for their habitat. Javan Hawk-Eagle (Nisaetus bartelsi) is an endemic raptor species in Java Island. Its existence is severely endangered due to declining population numbers, low reproduction rates, illegal hunting, and habitat fragmentation. Mount Halimun Salak National Park is one of the conservation areas with the highest Javan Hawk-Eagle population on Java. This area has deforestation, degradation, and forest fragmentation resulting in extensive declining of Javan Hawk-Eagle habitat. This research is to identify the priority areas that need to be restored for the movement of Javan Hawk-Eagle using the MaxEnt and Condatis applications. Variables used for habitat suitability maps were altitude, temperature, rainfall, distance from the river, distance from the road, and types of land cover. Habitat suitability maps were used to determine source and target areas. Data needed for priority areas of restoration using Condatis applications were habitat layer, source and target, reproduction rate, and dispersal distance. The priority area obtained was a comparison between the speeds of Javan Hawk-Eagle colonization between two scenarios called the Business as Usual and forest cover scenarios. The difference in speed of colonization of Javan Hawk-Eagle was the basis in determining priority areas for restoration.

Primates, the closest living biological relatives with human, play the important roles in the livelihoods, human-health, and ecosystem services. In the Anthropocene, populations of 75% of primate species are decreasing globally – due to cultivation activities, logging harvesting, hunting, and climate change. In this study, we focus on Bornean orangutan (Pongo pygmaeus) as the global conservation icons. Hence, understanding Bornean orangutan’s distribution dynamics is crucial regarding to conservation and climate mitigation strategies. The objectives of this study are: (1) to predict current and future spatial distribution of orangutan in Borneo using pessimistic climate model and land cover projection as well; (2) to identify spatial dynamics of Bornean orangutan distribution due to climate and land cover change in 2030. Species distribution modelling of baseline and future scenario was performed using logistic regression model. Land cover categories and climate parameters (i.e. annual temperature and precipitation) were used for model predictors. Presence points of observed primate species were retrieved from Ministry of Environment and Forestry Indonesia (MoEF). We used WorldClim v2.0 annual temperature and precipitation data for the baseline and CMIP5 MIROC-ESM model RCP8.5 2030 for the future climate scenario. We performed cellular automata algorithm to retrieve 2030 projected land-use for the future. Distance to road and distance to selected important land covers were used for transition potential modelling of land cover projection. Generally, the prediction shows that suitable habitat of Bornean orangutan will decrease in 2030. However, we found the gain of suitable area of Bornean orangutan. Findings of this study should support the identification of priority conservation area of Bornean orangutan for the future and wildlife corridor management planning.

Deforestation is one of the largest issues in Indonesian forests. Between 2001 and 2017, South Tapanuli, one of regency in North Sumatera Province, Indonesia, experienced the largest forest loss in 2011. One of the best way to improve the quality of deforested landscape is ecological restoration. In order to get the comprehensive understanding for landscape restoration planning, identifying the Environmentally Sensitive Area (ESA) should be considered. This study aimed to provide a foundation to support new decision-making tools for policymakers to plan the ecological restoration for South Tapanuli. Multi criteria approach combined with participatory planning of local stakeholders were applied for developing criteria. The criteria were selected based on biophysical characters derived from regulations and were converted into boolean map. Then, ESA model was obtained from overlaid of those boolean map which is identified the level of sensitivity area including level 1 (highest), 2, 3, and 4 (lowest). Sensitivity level 1 were dominated in Muara Batang Toru, Batang Toru, Angkola Selatan, and Angkola Sangkunur consisted of 5,340.78 ha (25,14%); 3,129.07 ha (14,73%); 2,911.29 ha (13,7%); and 2,708.51 ha (12,75%), respectively. More than 23% of oil palm in South Tapanuli were identified as being located in the forest zone. We proposed Muara Batang Toru, Angkola Selatan, Angkola Sangkunur, and Batang Toru as the top priority sub-districts for ecological restoration in South Tapanuli Regency. This study reccommends 50,848.85 ha should be managed immediately and full restoration will be proposed at 17,098.06 ha (33.87%).

Four years have passed since the declaration of West Papua as the province of conservation. As a follow up to the declaration, the implementation of development in West Papua should refer to the following principles i.e. environmental protection, preservation of biodiversity, management of utilization of natural resources wisely and sustainably, and recovery of the livelihoods and management of important ecosystem that have been degraded. Identifying Environmental Sensitive Area (ESA) should be considered as one of the first steps to realize spatial planning for a conservation province that must prioritize protection zones. Multi criteria approach combined with participatory planning of multi stakeholders were applied for developing criteria. The criteria were selected based on biophysical aspects derived from regulation (Presidential Decree 32/1990 on Protected Areas Management) and were converted into boolean map. Then, landscape models were obtained from overlaid of those boolean map which identified the degree of sensitivity including highly, moderately, and slightly. All three ESA models produce varying percentages of highly sensitive areas at 82.80%; 59.12%; and 32.21% for ESA model-1, model-2, model-3, respectively. Although varied, but found the same three regencies dominated by highly sensitive areas namely the Teluk Bintuni, Kaimana, and Tambrauw regencies. Highly sensitive area of the ESA model will be proposed as a protection zone as either the first or second priority to contribute for reducing disaster risk. Finally, ESA model can be used for giving reccomendation the existing regional spatial plan (RTRW) and of course taking into account the existing conditions of land cover

Changes in land use and land cover play a critical role, especially in the Lore Lindu National Park (TNLL) area, impacting on ecosystem functions. This study was aimed at analyzing the dynamics of land change and the factors influencing the land change in this National Park. The methods used were the GIS technique and a binary logistic regression model. The land changes locating between Sigi Regency and Donggala Regency, Central Sulawesi Province, Indonesia, which consisted of thirteen sub-districts in the TNLL region acquiring from Landsat satellite data acquisition of 1997, 2002, 2013, and 2018. The dynamics of land changes during the period of 1997-2002 has decreased the forest area by 2,643 ha, and the next period, 2002-2013, the decline of forest area has reached 4,265 ha, and the overall the dynamics of land change experiences a significant increase from 1997 to 2018 which declines the forest areas approximately 10,175 ha and followed by a decline of the meadow area in 1,726 ha changing function into the built-up land of 526 ha, mixed gardens of 1,189 ha, fields/moorings of 3,019 ha, rice fields of 1,548 ha and shrubs of 5,619 ha. The factors influencing the land change in this TNLL region based on the results of binary logistic regression analysis are the population density (X5), distance from the settlement (X3), distance from the road (X2), distance from the capital (X4), and topographic conditions (X1). Of these five variables, the population density has the highest negative regression coefficient, which is equal to -0.068. The regression equation is Y = -0.094X1-0.157X2-0.176X3-0.083X4- 0.068X5 and being significant in a level of 0.001 percent that indicates these five factors have influenced greatly high the land change in the TNLL. This situation can be inferred that the free distribution and population growth in the National Park influence increasing the conversion of forest areas.

The implementation of Integrated Coastal Management (ICM) requires concern from various sides. Semarang City is the main activity and economic center in the north of Central Java, Indonesia, and this status presents a significant challenge to realizing ICM based on coastal ecosystems, such as mangroves. This paper qualitatively examines the collaborative practice of various stakeholders in Semarang City in applying ICM to disaster risk reduction based on mangrove ecosystems. Intensive development has created a stumbling block to various stakeholders in mangrove ecosystems management. Also, this city is at high risk of coastal disasters like tidal floods, land subsidence, and social-economic problems of coastal communities. This situation requires the integration of various stakeholders to realize the ICM in the coastal areas of Semarang City effectively. In actualizing mangrove management practices as part of disaster risk reduction, some stakeholders are involved, namely the governments, universities, communities, and the private sector from the international, national, regional, and local level.

Remote sensing active systems which is represented by C-band Synthetic Aperture Radar (SAR) enhance features in mapping inundation of coastal areas of Jakarta that are free of clouds/shadows. This study was conducted to assess the dynamical inundation of coastal areas in Jakarta based on multi-temporal data of C-band SAR Sentinel 1A. Data was analyzed using In-SAR and Radar Polarization analysis. Data processing was performed using SNAP 6.0 and QGIS Las Palmas 2.18.15 software. The results of this study indicate that the backscatter coefficient of the surface water is about - 19dB. The polarization analysis shows the appearance of water bodies and surface water mixed with other objects that was blue and cyan colors. VH polarization analysis showed more detection than VV polarization analysis. Dual polarization analysis reveals inundation changes in certain areas such as coastal dikes, reservoirs, mangrove ecosystems and built-up land in temporally and spatially. This study demonstrates an ability of rapid assessment in monitoring coastal inundation of tropical urban areas using InSAR and Radar Polarization analysis.

Suspended sediments are part of factors affecting the sedimentation process. Hydrodynamic conditions, along with mangroves and ponds in the coastal area, affect their distribution in Wulan Delta. This study was designed to estimate the concentration of suspended sediments from 2016 to 2018 and analyze the distribution patterns in Wulan Delta, Wedug District, Demak Regency. It employed quantitative methods with quantitatively and qualitatively descriptive analyses. The parameters of oceanographic hydrodynamics, including wind, waves, currents, and tides, were used to examine the distribution of suspended sediments, while remote sensing images were used to estimate the concentration of these sediments. The hydrodynamic conditions in Wulan Delta were formed by the wind blowing at the speeds of 8.8-11.1 m/d and a frequency of 4.94% during the west monsoon, destructive waves with < 1 m in height, current velocities ranging from 0 to 25 cm/d, and mixed tides with prevailing diurnal type. From 2016 to 2018, the concentration of the suspended sediments varied between 60 and 180 mg/l, and their distribution followed the development of Wulan Delta, that is, leftward (southwest-south)

Oceanographic conditions, physical development, cultivation, and sedimentation in river estuaries are dynamic trends occured in Jepara Regency. These dynamics need to be understood so it is necessary to determine the position of the shoreline as an impact of morphodynamic to see the latest variations of the shoreline in Jepara Regency. Landsat imagery can be an alternative source of data for shoreline mapping, while shoreline extraction methods can be conducted using water index, which is easy to perform. Regulation published by the Head of the Geospatial Information Agency Number 6 of 2018 can be used as a standard for shoreline maps accuracy obtained from remote sensing imagery. The research objective is to map the Jepara shoreline using NDWI, MNDWI, and AWEI transformations and compare the water index performance. Shoreline data is extracted from Landsat 8 OLI imagery, while the reference shoreline for accuracy assessment is obtained from visual interpretation of PlanetScope imagery. Threshold 0 and subjective threshold based on experiments per coastal physical typology samples are used to separate land-sea. The difference in the shoreline length on the eight shorelines are due to the limited capability of the water index in obtaining the shoreline. MNDWI shoreline with a threshold of 0 gives the lowest RMSE value (RMSE= 25,33 m) among another index, while the NDWI shoreline with a threshold of 0 gives the highest RMSE value (RMSE= 43,77 m).

Indonesia is a country at the southeastern edge of the Eurasia Continent that has geologically complex conditions. Located at the meeting point of the world plates, this country lies in a zone of high tectonic activity. As a consequence, it is highly vulnerable to natural hazards, such as earthquakes and tsunamis. For instance, Ternate Island in the east of Indonesia is prone to tsunamis. This study intends to map tsunami susceptibility in the coastal areas of Ternate Island. The mapping stages were as follows: weighting and scoring of each parameter and, then, data processing based on Geographic Information System (GIS) using the overlay features in ArcGIS software. The results identified a higher susceptibility to tsunamis in the southern coastal area than the southwestern one. Several influencing factors of tsunami susceptibility on the island are elevation, slope gradient, and coastal material roughness.

The Lancang Island waters have the potential of marine biological resources such as the crab (Portunus pelagicus). The crab is a species that eats suspended material. Remote sensing can estimate the parameters of Total Suspended Solid (TSS). The purpose of this study was to estimate the distribution and test the accuracy of TSS concentrations in Lancang Island waters extracted from Landsat 8 OLI images by field observations. Statistical test indicators that can be used for accuracy tests include; root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE). The results of the RMSE value was 11.5 showed that the size of the error based on the difference between the value of the image and field data. The smaller the RMSE value, means the results of the model estimation produced was more precise with those observations. The MAE value of 1.774 showed the simplest form of error size. The MAE results indicated that it could be seen that the prediction error of the distribution of TSS was too small. It means the prediction of the distribution of TSS in this study had high accuracy. The NMAE of 31.9% shows the error rate that is normalized and expressed in percent (%). The NMAE value below 30% that could be used as proof of the validity of image data. The high error value was caused by differences in the time taken by field data with the recording time of satellite images and the effect of thin cloud cover.

Ocean tides are the phenomenon of periodic sea-level change on the coast or in the ocean. There are a number of basic requirements that should be considered when planning to record the tidal data in the field, such as the length, the location, and most importantly the interval of observation. Unique features of Indonesian waters induce several types of tide observed. Focusing on the applicability of altimetry satellites in obtaining tidal harmonics to demonstrate the advantages of it. Hiring altimetry data from Topex and Jason series from RADS server could derive 25 years data of SSH. Using Formzhal-equation, the construction of tidal types has done. Mixed prevailing diurnal and diurnal types commonly found in the western part of Indonesia, while mixed prevailing semidiurnal types appeared in the eastern part.

Machine learning classification in remote sensing imagery is considered capable of producing classification results with high accuracy in short processing times. This research was conducted with the aim of mapping the spatial distribution of benthic habitat on different types of coral reefs in the waters of Flores Island, NTT using PlanetScope image using Random Forest (RF) and Support Vector Machine (SVM) classification algorithm. Benthic habitat information from field surveys were used to train the RF and SVM algorithm and validate the classification results. The classification results indicated that Mesa Island, the Northern and the Western side of Labuan Bajo are dominated by seagrass beds, and on Bangkau Island is dominated by coral reefs and bare substratum. The highest overall accuracy of the RF classification results is 71.88% from West Labuan Bajo (fringing reef) result. Meanwhile, the highest overall accuracy of the SVM classification is 76.74% from Bangkau Island (patch reef) result.

Satellite imagery along with image processing techniques are essential tools for bathymetry retrieval since they are relatively inexpensive in term of time and budget in comparison to the conventional bathymetry survey. Approximately, 1126 bathymetric maps in various scales were produced by Badan Informasi Geospasial (BIG) covering Indonesia’s oceans and coastal waters. Those maps actually only cover 24,9% of total maps required to cover all Indonesia waters. Moreover, gaps in survey data, particularly in shallow water area, are also problematic. Only 12% of Indonesia’s waters are covered by a detailed survey. Hence, BIG is exploring remote sensing techniques to help with the improvement of Indonesia’s bathymetric maps including Satellite-Derived Bathymetry (SDB). SDB is promising due to its ability to fill the gap of depths derived from conventional hydrographic surveys. This is important for BIG since achieving wide-area depth coverage via the hydrographic surveys is costly in terms of time and money including for the safety of surveyors. This paper focuses on modelling bathymetry from multispectral images along Tanjung Kelayang, Tanjung Priok and Cilamaya shallow water area, Indonesia. A nonlinear machine learning technique was used to derive shallow water bathymetry by combining single beam echosounding measurements and the reflectance of red, green, blue, and near infrared bands of remotely sensed imagery. SVM can work better in a clear water area, for i.e. Tanjung Kelayang with RMSE less than 0.6. Furthermore, higher accuracies were obtained at depth range 0-5 m and 10-15 m for Tanjung Kelayang and Tanjung Priok, respectively. Highest accuracy was given in the depth range 0-5 m when applying SVM using four bands, but when using only three bands lowest RMSE value was obtained in the depth range of 5-10 m. SVM are able to estimate depth information in the shallow water area, especially in the water depth of <6 m (for i.e., Tanjung Kelayang and Cilamaya). However, the SDB results are noisy in the deeper water area >6 m. The inclusion of NIR band to the datasets results in a better accuracy. From results, we can see a correlation between the accuracy of bathymetric model and water clarity. High turbidity impacted the sensitivity of the depth algorithm. A complete data set containing water quality and benthic data is needed for further analysis to determine specific source of error.

Internal wave is one phenomenon that plays an important role in the turbulent process and mixing in the ocean. One area that has been known as a source of internal wave generation is the Lombok Strait. The study of the internal wave characteristics in the Lombok Strait has been carried out using satellite data, radar sensor or Synthetic Aperture Radar (SAR). Although the internal wave parameter well extracted and described from SAR imagery, however, the information related to the frequency of occurrence and its temporal distribution remains unclear because of the limited spatial and temporal resolution of SAR data used in previous studies. The unprecedented availability of SAR C-band images provided by the Sentinel-1A constellation offers the opportunity to propose a new approach of internal wave occurrence frequency distribution in the Lombok Strait. In this work, 4-year consecutive period SAR images from Sentinel-1A satellite examined. Annual and monthly distribution of internal wave occurrence frequency in the Lombok Strait is discussed. It is found that the yearly distribution of SAR-observed internal wave occurrence frequencies varies each year with the maximum frequency of 33% in the year 2015, implying that large-scale processes changing the SAR observed internal wave occurrence. The monthly SAR observed internal wave occurrence frequencies show the monthly variability with the high frequencies are distributed from March to September with a maximum frequency of 55%. The low occurrence frequencies are distributed in October and February with a minimum frequency of 30%.

The coastal area of Special Region of Yogyakarta (DIY) will experience rapid regional development after the functioning of the Southern Cross Roadway (JJLS) and New Yogyakarta International Airport (NYIA). In order to increase Regional Original Income (PAD) from the tourism sector, these developments must be welcomed with developments in the field of coastal tourism, especially in the form of ecotourism. The process of developing mangrove ecosystems for ecotourism in DIY must be synchronized with applicable government policies, especially policies that regulate spatial planning and development policies in the tourism sector. This study aims to analyze the existing conditions of mangrove ecosystems in the coastal areas of the Special Region of Yogyakarta. Mangrove mapping was carried out in four different locations, namely at Baros Beach, Kretek, Bantul Regency and Jangkaran Mangrove Forest Area, Kulonprogo Regency. Fieldwork consists of small format aerial photo data acquisition, Ground Control Point (GCP) and Independent Check Point (ICP) collection using Geodetic GPS, along with observations of mangrove conditions in the field. Post-field stages consist of various types of processes regarding processing field data and analysis, including orthophoto mosaics, accuracy calculations, and acquisitions of mangrove information such as area and distribution. The main benefit of this research is the availability of basic data on the existing conditions of mangrove areas in the Special Province of Yogyakarta. This basic data will then be used as a reference for managing the coastal environment and improving the economic conditions of the community through the development of mangrove ecotourism-based tourism. it also supports disaster risk reduction efforts through the development of mangrove ecosystems.

Random forest is a machine learning algorithm that can be used to improve the classification accuracy of mapping using remote sensing, especially for seagrass mapping in a complex optically water shallow. This research is aimed to map seagrass species composition and percent cover using random forest classification and regression using PlanetScope image. Optically shallow water around Labuan Bajo was selected as the study area. Sunglint and water column corrections were applied to the surface reflectance image. Principle Component Analysis (PCA) transformation was applied on surface reflectance bands, deDeglint bands, and depth-invariant index bands. These bands were used as the input band for random forest classification and regression algorithm, using field data to train the algorithm. Benthic field data was collected by the photo transect and seagrass field data was collected by the photo quadrat transect technique. Benthic habitat classification scheme was constructed based on the variation of benthic habitat insitu, which consisted of coral reefs, seagrass, macroalgae, and bare substratum. Seagrass species composition classification scheme was constructed following the variation of seagrass species insitu, which consisted Enhalus acaroides (Ea), Enhalus acaroides mixed Syringodium isoetilolium (EaSi), Enhalus acaroides mixed Thalassia hemprichii (EaTh), Halodule uninervis (Hu), Mixed species class, Thalassodendron ciliatum (Tc), Thalassodendron ciliatum mixed Enhalus acaroides (TcEa), Thalassia hemprichii (Th), Thalassia hemprichii mixed Cymodocea rotundata (ThCr), and Thalassia hemprichii mixed Syringodium isoetilolium (ThSi) class. Accuracy assessment using independent field data showed that random forest algorithm produced 63.57%- 72.09% overall accuracy for benthic habitat and 83.52%-85.71% overall accuracy for seagrass species composition. Random forest regression for seagrass percent cover produced R 2 between 0.78-0.81 with the error of prediction between 14.59-15.26.

Seagrass meadows have many ecosystem services to coastal areas and adjacent ecosystems, these services include nursery area for marine organisms, sea turtle feeding ground, and blue carbon sequestration. Therefore, it is important to protect seagrass in order to preserve their functions. Seagrass percent cover is one of the parameters to asses seagrass condition. Several approaches have been developed to map seagrass in optically shallow waters and one of them is by using remote sensing. This approach is more effective and efficient compared to field survey alone. The aim of this study is to produce seagrass spatial distribution and percent cover map using high resolution image. In this research, Support Vector Machine (SVM) classification and regression, one of the machine learning algorithms, was used to classify PlanetScope image using field data as training area to map seagrass spatial distribution and percent cover. The result show that SVM produced 73.98% overall accuracy for benthic mapping, with seagrass class producer’s accuracy and user’s accuracy is 93.71% and 85.35% respectively. Meanwhile, for seagrass percent cover, the SVM algorithm produced map with 26.48% standard error.

Sampling in the lab to get XTM640 data is part of the LWIR XTM640 camera calibration parameter. The XTM640 camera uses a 17μm pixel microbolometer sensor, at the time of operational data acquisition resistance changes according to the infrared radiation that affects it. XTM-640 is an extremely compact and versatile thermal camera module with unique image quality and stability for a broad range of OEM applications, it consists of an uncooled microbolometer. Uncooled microbolometer is known for its low cost. The method used in this paper is comparing the camera output sampling, to acquire how much the noise is, hence the result can be used as a future reference for calibration data.

The fourth generation of LAPAN satellite will employ Experimental LAPAN Line Imager for Space Application (ELLISA) for its mission on earth observation. In developing the imager, embedded systems like microcontroller and FPGA (field-programmable gate array) are utilized as interface and timing control, respectively. FPGA controls CCD (charge-coupled device) and ADC (analog-to-digital converter) timings and alters the data from CCD format to Camera Link format. Microcontroller, as an interface, handles command from users and other subsystems. Communication system is established between the two devices in order to transfer and translate incoming data from/to the subsystems and user. In this paper, a customized communication design has been successfully implemented between the microcontroller and FPGA for ELLISA development. This communication design can be realized on microcontroller with simple features.

Spectral reflectance of objects provides key recognition of objects from remote sensing data. Each surface objects have their own specific spectral reflectance pattern that acts as a spectral fingerprint for object discrimination. This study aims to evaluate the effectiveness of radiometric correction applied to WorldView-2 image by comparing the image correction result with field spectrometer measurement. Some objects were chosen as the basis for observing object spectral reflectance, namely grass, non-mangrove vegetation, mangrove vegetation, soil, and asphalt. The WorldView-2 image was radiometrically and spectrally corrected up to at-surface reflectance level using provided procedures. For reference, the spectral reflectance of selected objects were collected in the field using a JAZ EL-350 field spectrometer (340-1028 nm). In order to perform a direct comparison and evaluation, the results of object spectral reflectance of field spectrometer were resampled based on the center wavelength of WorldView-2 image bands (i.e. from thousand to eight bands). This study found that the spectral reflectance patterns of all targeted objects were similar. However, the most accurate spectral reflectance of WorldView-2 image object was asphalt. Asphalt has high colour homogeneity and relatively stable in a long period of time. This study shows that the standard image radiometric and spectral correction approach is effective to represent the spectral reflectance of objects on Earth surface.

LAPAN A2 is an Indonesian microsatellite built by Indonesia National Institute of Aeronautics and Space (LAPAN). It carries an Automatic Identification System (AIS) receiver as one of its payloads for ship monitoring with capability to record identity and position data of ships around the world along LAPAN-A2 satellite track. AIS data recorded by this satellite is acquired through S-Band frequencies using a S-Band receiver at Rancabungur Ground Station. An analysis is needed to determine the performance of the S-Band receiver. This paper provides quantity analysis of S-Band receiver by analyzing the AIS data that was acquired by the S-Band receiver. From this study, it can be concluded that the difference in the amount of data is not significant because the elevation used is also spread evenly from the smallest to the largest in terms of satellite elevation and acquisition duration.

Limitations of size and mass obstruct small satellites to have energy storage and generation freely. However, any satellite that brings SAR (synthetic aperture radar) payload requires much bigger energy storage compared to, for example, one that brings an optical payload for remote sensing mission. This paper addresses an energy balance analysis for microsatellite such that the remote sensing mission using SAR payload is successfully run. A specific LEO orbit, attitude of satellite, and position of solar panel are taken as a constraint in the simulation to obtain data used in potential solar power generated calculation. Potential solar power generated with specific dimension of solar panel is calculated by considering three solstice and equinox. In addition, the shadow that covers solar panel due to part of satellite is also considered. Energy balance analysis compares between potential solar energy generated by solar panel and energy consumption such that the SAR mission is achieved.

LAPAN-A2/Orari and LAPAN-A3/IPB satellites are LAPAN-made satellites which are successors of the previous LAPAN-made satellites, namely the LAPAN-TUBSAT satellite made in Germany, the LAPAN-A2/Orari and LAPANA3/IPB satellites launched with Indian PSLV rockets from the Satish Dhawan Space Center, Sriharikota, India in 2015 (LAPAN-A2/Orari) and 2016 (LAPAN-A3/IPB). As time goes by, the two satellites produced large amounts of digital camera image data, for now before software search data images of satellites designed, images results only stored in conventional folder this method makes difficult for operators to search images with certain specifications. LAPAN hoped that the imagery of the two satellites could be integrated in the storage and web-based search media, so the internal user and external user could easily access the image data. This paper describes the design of database storage media, web-based data search which have three function that is search data by location’s name, date range of data retrieval, and also search data images by types of product (camera image data from LAPAN-A2 or camera image data from LAPAN-A3). With this software it is expected that LAPAN services for digital camera images data to every user will be more effective and efficient.

The LAPAN Satellites are remote sensing satellite that will send satellite attitude data in real time mode and offline mode. The satellite attitude data is very important in a remote sensing satellite mission. For example, in the digital imager mission, operator use data attitude to take the image of targeted area with high accuracy. To make it easier for operators to control satellite attitude, they need a satellite simulator or satellite mock up that can show satellite attitudes in realtime or offline mode, especially in special missions. In addition, the dissemination division can also use the simulator to educate people about satellites. This paper discusses the working principle of the simulator and the structural design of the simulator. After the analysis, it is obtained that simulator uses the gimbal mechanism. The materials for the structure is 7075-T6 aluminum alloy. Meanwhile, the drive are three 17HS4401 stepper motor in each axis. The 17hs4401 stepper motor is strong enough to rotate all axes and the 7075-T6 aluminum alloy is strong enough and rigid to be used as a simulator structure.

Since launched in 2015, LAPAN-A3/IPB satellite has been collecting many images of the surface of the earth. These images were taken by multispectral imager carried by the satellite. However, these data are not completely utilized yet. Therefore, this paper presents the potential applications of LAPAN-A3/IPB satellite multispectral imager. Literature study has been being done to find the research using the data form other satellites where their payload is similar to LAPANA3/IPB satellite. Because of the multispectral imager of LAPAN-A3/IPB has four bands (red, green, blue, and nirinfrared), most of the applications found are the application based on vegetation indices. It is found that LAPAN-A3/IPB satellite data is possible to support green open space mapping in the city, assessing soil saline degradation, measuring colored dissolved organic matter absorption, predicting land use land cover change and its effect on soil erosion, asses protective role of coastal woody vegetation against tsunami, asses resource development footprints using remote sensing, detect lahar path, and monitor urban growth.

LAPAN-A3/IPB operated in Nadir Pointing Mode as normal mode. Maneuvers are carried out to maintain attitude at nadir or to change satellite’s attitude to aim at the target in Target Pointing Mode. The attitude of the satellite when carry out the mission must be as close as possible to the target’s attitude parameter in order to get accurate and good quality satellite image. When the attitude of the satellite is not the same as the target, a difference will be created and this should be evaluated regularly. Historical data on satellite attitude differences and other satellite operating parameters are stored in a telemetry logfile data. There are two types of logfiles, logfile-client and logfile-server. So far, logfile-client is used to evaluate satellite operations, but the logfiles are vulnerable to formatting changes, so more reliable logfile is needed, the logfile-server is suitable because there are no changes in formatting. Logfile- servers are raw data telemetry that are issued directly from satellites without any data processing, so processing data directly from the logfile-server will produce more reliable results. This paper will discuss a method for evaluating satellite operations by processing raw data telemetry from the logfile-server. Using the method, based on March- August 2019 telemetry data, the attitude performance of LAPAN-A3/IPB satellite show positive trend. Yaw, pitch, and roll value, as attitude parameter, the majority are in the range of 0° until 1°.

Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy.

Paddy is a strategic commodity in Indonesia. Paddy crop divided into hundreds of varieties with diverse characteristics. Therefore, information about the characteristics of each rice variety is needed. Also, several studies on the spectral characteristics of rice varieties have been carried out. These studies applied the vegetation indices approach to plant canopies. The aim of this study is detecting the spectral characteristics of rice varieties based on vegetation indices. Several vegetation indices, derived from Red, Green, Blue (RGB) bands, namely Excess Green Vegetation Index (ExG), Normalized Green Red Difference Index (NGRDI), and Visible Atmospherically Resistant Index (VARI). Paddy field image derived from Unmanned Aerial Vehicle (UAV) was carried out to analyzed three rice varieties namely Ciherang, IR 64, and IR 42. The result showed that three rice varieties in Bekasi Regency have diverse spectral characteristics. It evident from the spectral minimum-maximum value of each variety, especially using the NGRDI. Ciherang has the highest spectral value (at the beginning of growth) and IR 42 has the highest spectral value (at the middle and end of growth).

Land surface consists of land, water and vegetations/settlements. Digital terrain model (DTM) is an assumption of land surface without vegetations/settlements, while digital surface model (DSM) shows the whole existing objects within the topography. The objective of this study was to compare between DSM and DTM results within several ground check spots in Ngantru Hemlet. The research was carried out in several stages, i.e. preparation, determination of benchmarks (BM) (based on point BM of Geospatial Agency of Indonesia /BIG at Cepu East Java), determination of several ground control points (GCPs), acquiring aerial photographs in Ngantru Hamlet, and processing the aerial photo data. Existing data retrieval points in the area included locations without surface object and locations with such objects. The analysis showed a comparison of the results of aerial photo analysis with several existing observation locations.