This PDF file contains the front matter associated with SPIE Proceedings Volume 10767, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

We are seeing tree mortality increase in western U.S. forests and die-off events around the world caused by serve or interacting disturbances like logging, drought, wildfire and pine beetle infestation. Limiting our knowledge of how forests respond is a lack of data on functional vegetation states at the tree or stand level over long periods of time and broad regions. Moderate resolution satellite imagery can provide changes in percent forest cover but cannot resolve vegetation state changes (e.g. from conifer to deciduous forest). The high resolution of Planet’s Dove imaging technologies may provide an opportunity to capture response at fine scales. We aim to integrate Planet’s constellation of satellites with Landsat imagery to create a multi-scale network for forest monitoring. However, the uncalibrated nature of these systems and the variability of sensor characteristics across the constellation make this problematic. We conducted a limited investigation of radiometric and thematic data methods for linking vegetation properties across spatial scales from 3 to 30 meters. The greatest challenge arises from the variation in Dove sensor radiometric response (roughly +/- 10%) across the constellation and optical cross talk associated with their broad, overlapping Bayer filter response. Applying a spectral band adjustment factor to improve radiometric correlation requires knowledge of the actual spectral response of the sensors which is not readily available. Using a K-means clustering algorithm to bridge scales and minimize sensor differences had mixed results for low reflectance scene components – perhaps again the result of cross-talk between Dove sensor spectral bands.

Normalized Difference Vegetation Index (NDVI) values extracted from remotely sensed optical imagery are used ubiquitously to monitor crop condition. However, challenges in the operational use of optical imagery are well documented making it difficult to capture measures of crop condition during critical phenology stages when clouds obscure. This study investigates the integration of Synthetic Aperture Radar (SAR) and optical imagery to characterize the condition of crop canopies in order to deliver daily measures of NDVI during the entire growing season. Multitemporal C-band polarimetric RADARSAT-2 SAR data and RapidEye images were acquired in 2012 for a study site in western Canada. SAR polarimetric parameters and NDVI were extracted. The temporal variations in SAR polarimetric parameters and NDVI were interpreted with respect to the development of the canola canopy. Optical NDVI was statistically related with SAR polarimetric parameters over test canola fields. Significant correlations were documented between a number of SAR polarimetric parameters and optical NDVI, in particular with respect to HV backscatter, span, volume scattering of the Freeman Durden decomposition and the radar vegetation index, with R-values of 0.83, 0.72, 0.81 and 0.71 respectively. Based on the statistical analysis, SAR polarimetric parameters were calibrated to optical NDVI, creating a SAR-calibrated NDVI (SARc-NDVI)). A canopy structure dynamics model (CSDM) was fitted to the SARc-NDVI, providing a seasonal temporal vegetation index curve. The coupling of NDVI from optical and SAR imagery with a CSDM demonstrates the potential to derive daily measures of crop condition over the entire growing season.

Climate Change is now an undisputed fact (IPCC, 2007). There is a broad consensus on fact that cities have a special role in Climate Change, occupying an especially relevant role in Urban Heat Island (Oke, 1973). This scientific and technical consensus, however, does not seem to have influenced urban planning practice. The analysis of the UHI is today a fundamental element for the proper understanding of the primary factors of the contribution of cities to CC. The analysis of the structure of climate in Metropolitan Areas should enable the adoption of measures to mitigate the adverse effects of CC[J1].

This paper proposes the construction of a set of explanatory models of the UHI of the Metropolitan Region of Barcelona (MRB) aimed at assisting planners in taking measures that serve, at the level of territorial and urban planning, to mitigate the effects of climate change. The general objective of the research is to study, using remote sensing techniques as well as "in situ" measurements, how urban design affects in the generation of the Urban Heat Island (UHI), as well as the urban microclimate in general. Specifically, this paper seeks to clarify whether the design of green areas can mitigate the UHI.

The hypothesis is that morphology of public space plays a key role to control UHI. The research methodology consisted in: a) studying the urban and climatic parameters of selected areas; b) analyzing the spatial distribution of the LST using remote sensing technologies (Landsat 8); c) obtaining LST and LSAT through field work, during day and night time; and d) constructing a model of surface and air temperatures as a function of the different types of land cover, combining Remote Sensed data and in situ measurements, for each of the areas of analysis.

The Ball Aerospace Pipeline Damage Prevention Radar (PDPR) project evaluated the use of airborne synthetic aperture radar (SAR) to detect vehicles and equipment located within buried pipeline right-of-way areas but obscured from visual detection. The project included the configuration of a commercial dual-band SAR/EO system for airborne operations, hardware and software modifications to optimize SAR change detection processing, and the execution of multiple flight tests to characterize SAR performance for the detection of equipment obscured by vegetation. Flight tests were conducted in 2016 and 2017 using X-band, Ku-band and ultra-wide band (UWB) SAR in urban and rural environments. Targets in the open showed close to 100% detection performance while covered target results depended on the amount of vegetative canopy. Detection "through" vegetation was generally better using the UWB system, but vegetation gaps frequently allowed higher spatial resolution detections with the Ku-band system. While large equipment was frequently identifiable in the Ku-band SAR images, having coincident EO imagery proved critical for context and automated deep learning based object identification. The detection performance difference between open and covered conditions clearly illustrates how a collection plan that optimizes open viewing conditions increases the overall probability of detection. This research was performed in response to the Damage Prevention topic through the Technology Development in the Pipeline Safety Research and Development Announcement DTPH5615RA00001.

The Compact Hyperspectral Prism Spectrometer is being developed as a candidate imaging spectrometer technology for insertion into future Sustainable Land Imaging missions. The 2013 NRC report Landsat and Beyond: Sustaining and Enhancing the Nations Land Imaging Program recommended that the nation should “maintain a sustained, space-based, land-imaging program, while ensuring the continuity of 42-years of multispectral information.” In support of this, NASA’s Sustainable Land Imaging-Technology program aims to develop a new generation of smaller, more capable, less costly payloads that meet or exceed current Landsat imaging capabilities. CHPS meets these objectives and will provide continuous visible-to-shortwave spectroscopic information at high spectral resolution. CHPS supports continuation of legacy Landsat data products as well as providing additional spectral information for a broader range of land science products. CHPS features full aperture full optical path calibration, exhibits high uniformity, extremely low straylight, and low polarization sensitivity. These are critical for meeting the demanding SLI measurement objectives. In preparation for space-borne instrument development, Ball is currently developing an airborne instrument that will provide representative spectroscopic data and data products. We are now in year 2 of a 3-year program and anticipate conducting initial airborne engineering flights in 4th-quarter 2018.

Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008-2011 and 2017. A total of 2,370 lakes across the contiguous United States were included in the analysis. Bloom percentage was calculated for nine United States climate regions to examine regional patterns. Changes in cyanobacterial bloom percentage followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a peak around October, and decreased through the winter. Wintertime data, particularly in the northern latitude regions, was consistently limited due to snow and ice cover. With the exception of the Southeast and South climate regions, regional patterns mimicked patterns found at the national scale. The Southeast and South regions exhibited an unexpected pattern as cyanobacterial bloom percentage peaked in the winter rather than the summer. Several environmental factors and potential satellite limitations can possibly explain these findings. Results from this research can help establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States.

The gradual spread of urbanization, the phenomenon known under the term urban sprawl, has become one of the paradigms that have characterized the urban development since the second half of the twentieth century and early twenty-first century. The arrival of electrification to nearly every corner of the planet is certainly the first and more meaningful indicator of artificialization of land. In this sense, the paper proposes a new methodology designed to identify the highly impacted landscapes in China based on the analysis of the satellite image of nighttime lights.

The night-lights have been used widespread in scientific contributions, from building human development indices, identifying megalopolis [2] [3] or analyzing the phenomenon of urbanization and sprawl [4], but generally they have not been used to forecast the urbanization in the near future. This paper proposes to study the urbanization impact in China between 1992 and 2013, and models a hypothesis of future scenarios of urbanization (2013-2025). For this purpose, the paper uses DMSP-OLS Nighttime Lights (1992 – 2013). After obtaining a homogeneous series for the whole period 1992- 2013, we proceed to model the spatial dynamics of past urbanization process using the "urbanistic potential" of each of the 13.7 millions of analyzed cells. This model allows to design a probable growth of the urbanization phenomenon between 2013 and 2025 as well to predict a progressive displacement of the urbanization from east coast to mainland and west, in congruence with the current demographic models [5].

Surface ozone can trigger many health problems for human (e.g. coughing, bronchitis, emphysema, and asthma), especially for children and the elderly. It also has harmful effects on plants (e.g. chlorosis, necrosis, and yield reduction). The United State (U.S.) Environmental Protection Agency (EPA) has been monitoring surface ozone concentrations across the U.S. since 1980s. However, their stations are sparsely distributed and mainly in urban areas. Evaluation of surface ozone effects at any given locations in the U.S. requires spatial interpolation of ozone observations. In this study, we implemented two traditional spatial interpolation methods (i.e. triangulation-based linear interpolation and geostatistics-based method). One limitation of these two methods is their reliance on single-scene observations in constructing the spatial relationship, which is prone to influence of noisy observations and has large uncertainty. Deep learning, on the other hand, is capable of simulating common patterns (including complex spatial patterns) from a large amount of training samples. Therefore, we also implemented three deep learning algorithms for the spatial interpolation problem: mixture model network (MoNet), Convolutional Neural Network for Graphs (ChebNet), and Recurrent Neural Network (RNN). The training and validation data of this study are the 2016 EPA hourly surface ozone observations within ±3-degree box centered at the Billings, Oklahoma station (USDA UV-B Monitoring and Research Program). The results showed that among the five methods, RNN and MoNet outperformed the two traditional spatial interpolation methods and RNN has the lowest validation error (mean absolute error: 2.82 ppb; standard deviation: 2.76 ppb). Finally, we used the integrated gradients method to analyze the attribution of RNN inputs on the surface ozone prediction. The results showed that surface ozone observation is the most important input feature followed by distance and absolute locations (i.e. elevations, longitudes, and latitudes).

Previous remote sensing studies of intelligent feature extraction led to the successful image fusion, merging, and cloudy pixel reconstruction destined for the spatiotemporal change detection. Based on fused satellite images with better spatial and temporal resolution, this study explores a thorough comparative analysis in terms of feature extraction capability of deep learning, regular learning, fast learning, and ensemble learning relative to some traditional feature extraction algorithms (2-band and linear regression models). In specific, this study aims to evaluate the systematic influences of fast and deep learning models with potential to create a new ensemble learning tool for better feature extraction based on fused remote sensing images. In ensemble learning step, the whole ground-truth dataset is fed into the selected ensemble learning algorithm (i.e., a classifier fusion algorithm) with the aid of singular value decomposition to create an integrative tool. Practical implementation was assessed by a case study of water quality monitoring over dry and wet seasons in Lake Nicaragua, Central America. Both deep and fast learning algorithms outperform the regular learning algorithm with a single layer forward network and ensemble learning may take advantage of merits of deep, fast, and regular learning algorithms. Final water quality assessment was generated based on the integrative algorithm of the two with bio-optical models for eutrophication assessment in Lake Nicaragua. Although deep learning has better results in validation and the ensemble learning model aggregates different types of strength from all models based on all limited ground-truth samples.

The spatial and temporal distribution of atmospheric pollutants (especially PM_2.5), which may cause adverse effects on human health and the environment, are affected by urban vegetation through deposition and dispersion processes. Although lots of studies have been conducted to investigate this effect, we still lack the knowledge of how urban vegetation reduces PM_2.5 and other air pollutions quantitatively. In this study, Landsat 8 data are used to retrieve the urban surface parameters (including Leaf Area Index and Normalized Difference Impervious Surface Index). Meanwhile, hourly PM _2.5 and other air pollutions concentrations over a period of one year (from Jan. 1, 2016 to Dec. 31, 2016) were obtained at a real-time air quality monitoring system in Shenzhen, P.R. China. It is found that, urban vegetation has little effect on air pollutants. Meanwhile, the results show that, a strong relationship between impervious surface and air pollutants are found.

Particulate matter (PM) is one of the main pollutants in the atmosphere, which is harmful to human. PM10 and PM2.5 became the main subject attracting more and more interest. To compensate the weakness of conventional observation method, application of remote sensing tools have been widely used in environmental monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has a high temporal resolution, which, at present, is an ideal data source in simulative monitoring of regional-scale environment changes. In this study, we focused on PM2.5 and AOD (Aerosol Optical Depth) in coastal areas. Correlation between each two of them was analyzed. From the daily average value year of two sites PM2.5, the concentration of air particulate pollutants is low before and after summer, and the heating season is higher in winter and spring. The average PM2.5 concentration value of 2014 and 2015 is 50.11μg/m3 and 41.11μg/m³ respectively in Fushan station, and that of the Laishan station is 45.63μg/m³ and 38.73μg/m³ respectively. From the interannual variation, the concentration of air particulate pollutants in the two regions has a tendency to decrease. According to the new standard of air quality of the PM2.5 monitoring network, the air quality of the vast majority of dates belongs to the excellent grade. In light of the policy of air pollution, the PM2.5 concentration in 2015 was lower than that in 2014. Due to the complexity of atmospheric components and their interactions, and the spatial and temporal constraints of PM2.5 detection resulted in a low correlation between the AOT and PM2.5.

Crop classification can accurately estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. The crop yield information is an important basis for the country to formulate food policies and economic plans, so the study of crop classification is of great significance. Traditional optical remote sensing is susceptible to sunlight and clouds, and Synthetic Aperture Radar (SAR) can be used all-time and all-weather. Compared to single- polarization SAR, full-polarization SAR has more abundant information. In this paper, C-band GF- 3(GaoFen-3) satellite data and multi-temporal Sentinel-1 data were used as data sources. Changchun City in northeastern China is selected as the experimental area and the scattering characteristics of typical crops in this area are analyzed. Firstly, the GF-3 and multi-temporal Sentinel-1 SAR data were preprocessed. Then, polarization decomposition of GF-3 data was performed to obtain three polarization characteristics: scattering angle, entropy and anisotropy (H/α/A). The Supports vector machine (SVM) algorithm was implemented as the classifier. Polarization characteristics, multi-source and multi-temporal SAR were used for classification features. The overall accuracy reached 91.9537%, nearly 10% higher than using full-polarization information alone, and the kappa coefficient was 0.8827. It shows that multi-source and multi-temporal SAR has obvious advantages in crop identification.

Forests play an important role in the global carbon cycle and natural air conditioning. Monitoring and mapping of forest distribution are of great significance. With the successive launch of new synthetic aperture radar (SAR) sensors, microwave remote sensing data acquisition methods have been developed from single-band, single-polarization and single-angle to multi-frequency, multi-polarization, multi-angle, multi-temporal and so on. That provides an unprecedented potential and opportunity for SAR in the research and application of forest identification. In this paper, the data source mainly included the quad-polarization C-band GaoFen-3(GF-3) and dual-polarization L-band ALOS-1 PALSAR. First, the single-look complex (SLC) data was preprocessed with multi-look, filtering, radiation calibration, geocoding, registration and clipping. Three polarization characteristic parameters of entropy (H), scattering angle (α) and anisotropy (A) were obtained by using Cloude-Pottier polarization decomposition, and three texture features of the mean (MEAN), variance (VAR) and dissimilarity (DIS) were extracted based on the gray-level co-occurrence matrix(GLCM). Combined with the advantages of GF-3 high-resolution quad-polarization and PALSAR L-band, multi-dimensional information including frequency, polarization, temporal and texture features was used synthetically. Then support vector machine (SVM) supervised classifier was used to obtain the four classification results, including coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and others. The experimental result shows that proposed method achieved a better classification result based on multi-dimensional POLSAR, the overall accuracy of forest type identification is approximately 89.47% and the Kappa coefficient is 0.85.

People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO₂ and SO₂ in 2015. Moreover, the ground-level NO₂ and SO₂ concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO₂ and SO₂ are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO₂ and SO₂ concentrations based on satellite observations is reliable with the validation R² 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO₂ and SO₂ has a similar characteristics to the satellite columns.

Tidal flats in Rudong county, located at the end of Jiandsu Radail Sand Ridges Area. Plentiful quantities of tidal flat makes this area an important land reserve resource. However, traditional field measurement technology encounters difficult due to special form of geomorphology in Rudong tidal flat, resulting the lack of data support in rational exploitation. In order to obtain the extent of tidal flat with volatile coastal evolution, we proposed a modified method, which based on the previous studies, to map tidal flat area with rare manual intervention. Firstly, a confident low tide image generated under the method of pixel-based NDWI average composite. Then, OSTU method was used to compute threshold, which used to segment image into two value. followed by tideline extraction. Subsequently, the extent of tidal flat in Rudong county was obtained. The study shows that the method can realize the extraction of the tidal flat extent in complex landform quickly and accurately. The research data can be obtained free of charge, which makes the method generalized.

Fires are a problematic and recurrent issue in Mediterranean forest ecosystems. Accurate discrimination of burn severity level is fundamental for the rehabilitation planning of affected areas. Though fieldwork is still necessary for measuring post-fire burn severity, remote sensing based techniques are being widely used to predict it because of their computational simplicity and straightforward application. Among them, spectral indices classification (especially difference Normalized Burn Ratio–dNBR- based ones) may be considered the standard remote sensing based method to distinguish burn severity level. In this work we show how this methodology may be improved by using land surface temperature (LST) to enhance the standard spectral indices. We considered a large wildfire in August 2012 in North Western Spain. The Composite Burn Index (CBI) was measured in 111 field plots and grouped into three burn severity levels. Relationship between Landsat 7 Enhanced Thematic Mapper (ETM+) LST-enhanced spectral indices and CBI was evaluated by using the normalized distance between two burn severity levels and spectral dispersion graphs. Inclusion of LST in the spectral index equation resulted in higher discrimination between burn severity levels than standard spectral indices (0.90, 8.50, and 17.52 NIR-SWIR Temperature version 1 vs 0.60, 2.83, and 6.46 NBR). Our results demonstrate the potential of LST for improving burn severity discrimination and mapping. Future research, however, is needed to evaluate the performance of the proposed LST-enhanced spectral indices in other fire regimes, and forest ecosystems.

The reliability of the measurement of ultraviolet radiation has always been a hot spot of research. The observation of ultraviolet radiation is not only affected by the solar elevation angle, aerosol thickness, ozone, dioxide, there is also a great connection with the systematic error of the measuring instrument. In fact, in the ultraviolet radiation observation, due to the lack of routine maintenance and periodic calibration, the radiation meter will obviously decline after a period of time, and the longer the use time, the more obvious the attenuation. Therefore, in order to obtained the consistent time series of the stable observational values, some reasonable methods must be adopted to correct the measured values. The data source of this research was part of the UV-MFRSR type ultraviolet radiometer observations from 2003 to 2010. These data were obtained by these daily time series calibration method. In theory, these time series points represent the response time of the instrument, and they should be stable for several months or even years. However, the performance of the in-situ calibration method was influenced by the aerosol / ozone loading mode in practice. The purpose of this study was to get a smooth observation curve by eliminating some observational anomalies. In addition, the actual data in the observation process, some date data is missing, so the reasonable prediction model is used to estimate the value of these data. In this paper, the ARIMA and GARCH models were used to predict the missing data and compared between the predicted value and the true value, it is found that the fitting degree of the predicted value and the true value based on the AR-GARCH model is higher.

Fine particles less than 2.5 microns in aerodynamic diameter (PM2.5) has found to threat human health and environment. The formation and diffusion of PM2.5 are closely related to the meteorological elements. Many scholars have studied the influence mechanism of meteorological elements to PM2.5. However, most of these researches mainly focus on some serious short-term atmospheric pollution, long-term research is rare. In addition, the impact of meteorological elements on PM2.5 has regional characteristics. This paper takes Shanghai as study area, applying PM2.5 concentrations from China environmental monitoring stations and reanalysis meteorological data from 2014 to 2016.. Through qualitative and quantitative analysis, this paper got the change characteristics of PM2.5 in Shanghai in recent three years, and the correlation between PM2.5 and relative humidity, temperature, wind and boundary layer height. Relative humidity is positively correlated with PM2.5, while U wind is negatively correlated with PM2.5. And there are seasonal differences in the correlation between PM2.5 and temperature, V wind and boundary layer height.

For the past few years, the aerosol pollution in Shanghai is getting worse, leading to the haze weather and air quality deterioration as well. This paper is a comparative study on reliability and applicability of the spatial interpolation methods for the regional air quality evaluation, the daily data of the air quality indices (AQI, PM_2.5 and PM₁₀) comes from the Shanghai automatic monitoring stations, which helps us to compare the different interpolation methods in testing and measuring various air pollutants in Shanghai. Inverse Distance Weighted (IDW), Spline and Kriging were respectively used for the calculation of spatial interpolation. With the aforementioned methods we can compare the interpolation methods and gain the four indices, such as the mean error (ME), the mean relative error (MRE), the root mean squared error (RMSIE), and the correlation coefficient (R²) , which help us make a comprehensive comparative analysis of the spatial interpolation methods for the Shanghai regional air quality. The result shows that the IDW method is optimal for PM_2.5 concentration and AQI, while Kriging Method is the Best for the concentration of PM₁₀. We can also find that Seasonal characteristics and different spatial aggregation characteristics have a significant impact on the interpolated results of air pollutants.

Based on the current land uses of 2005 and 2015 in Dongying City, this study obtained the spatial-temporal variation matrix of land use of the two periods with the overlay function in ArcGIS 10.2. The analysis results showed as follows: 1-Agricultural area decrease a little. 2-The urban land area had increased greatly. 3-Coastal aquaculture area increase a lot. The main driving factors were: 1-The policy of farmland protection was carried out in Dongying City. 2-Dongying City economic has developed rapidly in recent 10 years. 3-Driven by higher economic profits. The conclusions were meaningful for the reform of land use structure and the reform of economic structure in the future in Dongying City.

With the rapid development of urbanization, the dynamic evolution of urban expansion has become one of the hot topics throughout the world. Thus, modeling and predicting the urban expansion in the future is one of the effective methods for the study of urban growth. Based on the rapid urbanization in Shanghai, our study uses four years of land use data (1995, 2000, 2005 and 2010), DEM and two years of traffic roads data (2005 and 2010) to obtain the optimal parameters of urban growth through model calibration. And the results of calibration were used to simulate and predict the land use change in 2040 under different scenarios of excluded layers. The results show that the urban growth in Shanghai is more often grow along the edge of existing urban centers and the transportation network with the relatively high spread coefficient (43) and road coefficient (66), while the dispersion, breed and slope coefficient are relatively low. The SLEUTH simulation with these five parameters possessed satisfactory capability of predicting land use changes with the kappa coefficient of 0.8628 and an appropriate Lee-Sallee index of 0.8139. The result shows that the urban areas in Shanghai increase significantly in 2040, while the rural area, grass and other construction area are decreased. Therefore, SLEUTH can better predict the spatial changes of land use and provide some theoretical support and decision-making basis for the urban-rural planning in Shanghai.

Based on the data of Landsat remote sensing images in 2005 and 2015 in Weihai City, this paper referenced China coastal zone land use classification system and used the Arcgis10.2 software to construct the land use database by visual interpretation , and then analyzed the spatial and temporal changes of land use in Weihai City for 10 years. The results showed that： (1) The total area of the land use in Weihai City had been enlarged, mainly by mariculture expansion with land area of 11.3 thousand hectares.(2) The land use changing rates were fast, among which the unused land attitude was the largest at 24.85%.(3) The area of these land uses change was increased, which are forest land, urban construction land and the independent industrial and mining and traffic construction land. While the arable land and grass land areas decreased. (4) The main driving forces were its economic development and its national economic and social development planning. May this paper provide some references to its regional land use sustainable development.

The changes of land use affect the sustainable development of society through its influences on the interactive balance among the population, resource, environment and ecological development. In the process of Chinese urbanization, increasingly serious contradictions between human and land have been caused by the dramatic increase in the demand of land resources. This paper used a case study on Guian New District, which is a national-level new district in China. The research focused on the change of land use in the new-style urbanizing process of Guian New District. The sustainable development in this district was analyzed by applying the technology of Remote Sensing and Geography Information System to collect the spatial data of land use in 2010 and 2018 of Guian New District, utilizing the theory of ecosystem service value to obtain quantitative description of the ecological outcome of land use, and comparing the variance in land use and ecological benefit before and after the establishment of Guian New District. The study has shown that the land use of Guian New District almost meets the basic requirements of sustainable development. Furthermore, in order to achieve sustainable development in this district, suggestions were provided on how to improve the structure and location of land use, as well as taking account of the impacts on the long-term ecological benefit.

Time-of-flight (TOF) Lidar is widely used in capturing three-dimensional (3D) structure and reflectivity information. For using Geiger-mode avalanche photodiode (Gm-APD) and the technique time correlated single photon counting (TCSPC), a direct-detection 3D imaging lidar has high sensitivity in low-light-level (LLL) scene. Traditional method needs long fixed dwell time to collect tens of thousands of photons to find accurate range and mitigate Poisson noise at each pixel. We present a method that acquires accurate depth and intensity images using a small amount of detected echo photons and having quantitative analysis to estimate whether results are in the confidence interval. Based on prior knowledge that the echo signal is in the shape of emitted laser, we use one or two orders of magnitude back-reflected photons less than traditional method, fitting a curve of laser-return pulse by nonlinear least-squares fitting in order to obtain the range. The condition of moving to next pixel in our method is acquiring a fixed number of back-reflected photons, instead of sampling for a fixed time. This adaptive jump condition is able to speed up the scanning without more distortion. The results are analyzed with chi-square test to determine if the curve we fit has enough credibility. This quantitative analysis provides an important judgment condition for our method of fitting curve to recover the depth image. Experimental results demonstrate that our method is able to obtain the millimeter accuracy depth image in the confidence interval using hundreds of photons and increases photon-efficiency more than 10-fold over traditional method. Thus our method will be useful in LLL scene, such as military reconnaissance and remote sensing.

Since 2008, the Green Tide has been continuously erupted for 10 years in Yellow Sea. Relevant studies have proved that the source of the green tide burst is the laver rafts in the radiated sand area. In this study, UAV (Unmanned Aerial Vehicle ) and S2A (Sentinel Satellite) data were used to monitor and estimate the biomass of Green tide algae on the rafts of seaweed. Using UAV imagery combined with high-resolution satellite data and field survey data, Accurately monitoring and assessing the biomass of green tide algae in the radiation sandy area can provide a scientific basis for the prevention and early warning of the Southern Yellow Sea green tide disasters.

Air pollutants can cause serious effects on human health, especially fine particles in air, which can easily cause respiratory diseases, such as asthma and bronchitis, and also increase the probability of lung cancer and heart disease. The disease surveillance of the residents around the chemical plant is an effective means to understand the effects of the discharged pollutant on the health of the surrounding residents. The research area, Shanghai Chemical Industry Park is located at the junction of Jinshan and Fengxian, as the South Center of the six industrial bases of Shanghai. Applying the spatial distribution of population obtained from the extraction of residential land by using GF-2 data, and the disease data after cleaning treatment, can reach to the spatial distribution of the above two diseases. Through the integration of spatial characteristics and attribute characteristics, the disease surveillance of the surrounding residents can be realized, which directly reflects the impact of chemical industry on the health of the surrounding residents.

Unmanned aerial vehicle (UAV) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. In this paper, the wetland vegetation information is extracted from UAV remote sensing images by object-oriented approach. Firstly, the images are segmented and images object are build. Secondly, VDVI, VDWI, spectral information and object geometry information of images objects are comprehensively applied to build wetland vegetation extraction knowledge base. Thirdly, the results of wetland vegetation extraction are improved and completed. The results show that better accuracy of wetland vegetation extraction can be obtained by the proposed method, in contrast to the pixel-oriented method. In this study, the overall accuracy of classified image is 0.968 and Kappa accuracy is 0.934.

Sea surface temperature (SST) is an important factor that affects the changes of marine fishery resources. In this paper, the characteristics of distribution and variation in sea surface temperature was retrieved in northwestern Pacific Ocean by MODIS from 2008 to 2017. The results showed that the distribution of SST in northwestern Pacific Ocean was found to be characterized by regional and seasonal changes. Annually, periodical changes in SST was found unconspicuously, and spatially, the SST high value area showed a trend of moving from high-latitude to low-latitude. In August each year, there seemed to be a temperature boundary at 40°N, and the boundary will move south in September. Finally, we analyzed the SST distribution of the two main fishing periods of Cololabis saira in August and September each year, and preliminarily explained the cause of the "fish shortage" of saury recently years in Japan. The long-term variations in SST were discussed macroscopically in this paper, and this could give a new insight into fishing industry research in the Northwest Pacific.

As an important complement to satellite observation, the technique of Unmanned Aerial Vehicle (UAV) shows great advantages because of its high spatiotemporal resolutions, low cost and risk. With the development of technology related to UAV, its research was increasingly enhanced and has been applied to many fields such as environmental monitoring. Taking a coastal zone of Yantai as test area, this paper studied how to utilize the UAV system to monitor contaminated water in coastal zones. The results show that the contaminated water information can be extracted from the UAV remote sensing image. The multi-time monitoring conducted in this study can monitor the change of polluted water. This will provide technical support for the monitoring and treatment of polluted water bodies.

In Taiwan, hazard susceptibility maps of the Provincial highway routes 8 and 9 were produced by the Fourth Maintenance office, Directorate General of Highways, and the Ministry of Transportation and Communications (MOTC) in 2014 and 2015. The maps show that there are 235 road sections marked as high-potential hazard locations along route 8, and there are 80, 44, 34, and 7 locations classified as high, moderate, low, and very low-potential hazard locations, respectively, along route 9.

Based on the hazard susceptibility maps, we verify and discuss the occurrence of landslides on the two routes using the landslide inventories during 2015–2017. The results show that: (1) at 129 locations along route 8, 314 landslides have occurred, the 158 landslides that have occurred at the other 79 locations are not shown on the hazard susceptibility maps; (2) 256 landslides have occurred along route 9 in the past three years. 181 landslides have occurred at 49 high-potential hazard locations; 26 landslides have occurred at 15 moderate-potential hazard locations; 46 landslides have occurred at 12 low- potential hazard locations; 3 landslides have occurred at 2 very low- potential hazard locations.

From the results, we suggest further investigations is required of the hazard susceptibility maps of the Provincial highway routes 8 and 9.

The statistical forecasting model based on time series is one of the main means of sea level forecasting at present stage. However, the mechanism of sea level change is complex. The traditional method has some limitations for non-stationary nonlinear time series forecasting, and the prediction accuracy needs to be further improved. In this paper, we use the monthly mean tide level series from Zhapo Station (1959 ~ 2011), and combine the Ensemble Empirical Mode Decomposition(EEMD), Genetic Algorithm (GA) and Back Propagation (BP) Neural Network to propose a improved EEMD-GA-BP method for regional sea level change prediction. In this study, the EEMD method was used to decompose the original series and generate multiple intrinsic mode functions (IMF) according to different spectral characteristics of signals implied in the tide level series, to stabilize the time series, and improve signal to noise ratio. GA is used to optimize the weights and thresholds of BP Neural Network, due to the difficulty of determining the initial weight and threshold in BP Neural Network. Taking each IMF as the input factor of BP Neural Network, the future trend of each IMF is predicted respectively. Finally, the output of the IMF is reconstructed to obtain the predicted value of the original series. The results show that EEMD can effectively extract multi-time scale signals implicit in the series. BP Neural Network optimized by GA can well predict the future trend of sea level. Compared with the direct use of BP Neural Network algorithm, the use of EEMD for non-stationary non-linear time series smoothing, noise reduction and other processing can effectively improve the prediction accuracy. The use of GA optimize BP Neural Network can improve the accuracy. The EEMD-GA-BP algorithm provides a realistic meaning for the prediction of regional sea level change.

Paddy field is a major source of methane (CH₄) emission. Methane emission in paddy fields accounts for 31.5% of agricultural methane emissions in China. Double-rice cropping system is a part of the major paddy systems in China for rice production, accounting for only 27% of the national rice planting area while CH₄ emission accounting for 60% of the national CH₄ emission. Given the importance of reducing CH₄ emission from double rice to mitigate climate warming, it is necessary to investigate the impact of climate change on CH₄ emission of double cropping paddy field in the future. In this study, the denitrification–decomposition (DNDC-a process-based biogeochemistry model) model is employed to simulate the CH₄ emission from double-rice cropping system in southern China based on the historical meteorological data of the past 50 years (1966-2015) and the observational data of rice agricultural stations in the study area. Then we combined the outputs with Geographic Information System (GIS) technology to analyze the impact of climate change on CH₄ emissions from the double rice paddy. The results indicate that change of the average temperature is associated with the change of CH₄ emission across the growing period of double rice paddy. Methane has increased by 8.4% in the main producing provinces of double cropping rice in southern China. Zhejiang has increased by up to 20.8%. Anhui, Hubei, Hunan has increased by 10.6%, 10.2% and 11.4%. The relatively small increase in Fujian and Yunnan is only 5%. However, in the low latitudes of Guangxi, and Guangdong province, there was a slight reduction in CH₄ emission.

Based on GOCI data and the built-in CO2 algorithm, this paper investigated the spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai from 2014 to 2016. Results showed: The chlorophyll-a concentration in the study area has a significant spatial-temporal characteristics, showed a decreased tendency from estuary to offshore area in general. While the lowest value major in the north open seas, the highest value appeared in Sishili Bay and the coastal zone along Weihai, even extended to the western coastal area of Shandong Peninsula. The spatial difference of the concentration of chlorophyll-a in summer was significantly higher than that in winter, and the enrichment effect increased with the increase of temperature. From the perspective of temporal distribution, the chlorophyll-a level was highest in August and lowest in February, and there are small but obvious double peaks in the spring and autumn of May and October. Our work indicated that chlorophyll a concentration level in the study area showed a gradual upward trend in recent 3 years.

Chinese yam (Dioscorea opposita Thunb.) is consumed and regarded as medicinal food in traditional Chinese herbal medicine, Chinese medicinal yam especially is one of the most important Chinese herbal medicines and its medicinal needs have been increasing in recent decades¹. Furthermore, Chinese medicinal yam is susceptible to climate conditions during the growth period. Therefore, a better understanding of the suitability regionalization of Chinese medicinal yam under the impact of climate change is of both scientific and practical importance to spacial development and reasonable layout of Chinese yam in China. In this study, based on the Coupled Model Inter-comparison Project, Phase 5 (CMIP5) climate model projections with 5 Global Circulation Models (GCMs) developed by the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs), we assessed the changes of potential planting area of Chinese medicinal yam between the baseline climatology of 1981-2010 and the future climatology of the 2050s (2041-2070) under the RCP 4.5 scenario by the Geographic Information System (GIS) technology. Results indicate that regions with high ecological similarity to the Geo-authentic producing areas of Chinese medicinal yam include northeastern Henan, southeastern Hebei and western Shandong, mainly distribute in the lower reaches of the Yellow River basin and other major floodplains. In the future, the climate suitability of Chinese medicinal yam in these areas will be weakened, but that will still be the main suitable planting regions.

The accuracy of the temperature and humidity profiles is important for the atmospheric duct estimation, which is a special atmosphere layer for the radio-wave propagation. In order to use the dataset of satellite to monitor the atmospheric duct, we compare the temperature and humidity profiles between the radiosonde observation data (RAOB) and the NOAA-Unique CrIS/ATMS Product System (NUCAPS), and analyze the result of the atmospheric duct. Results show that the retrieved temperature and humidity profiles have higher accuracy under various weather conditions. However, when the RAOB data can calculate the atmospheric duct, the inversion profiles are difficult to monitor the same situation. The temperature inversion and humidity’s sharp decrease with height are the main synoptic conditions for the formation of atmospheric waveguides. Currently, the temperature and humidity profile of satellite inversion still lack capturing of turning point information. In order to effectively improve the application of satellite inversion data in atmospheric duct estimation, it is necessary to strengthen the profile’s vertical resolution and humidity inversion accuracy.