FASAL: an integrated approach for crop assessment and production forecasting
Author(s):
Jai Singh Parihar;
Markand P. Oza
Show Abstract
India has a very well developed system for collection of crop statistics covering more than 50 crops at village
level and aggregating it at different administrative levels. However, need for early and in-season crop production
forecasting has been strongly felt. Remote sensing for crop assessment has been explored since very beginning of space
applications in India. A nation-wide project called Crop Acreage and Production Estimation (CAPE) was launched at the
behest of Ministry of Agriculture, Government of India in 1988. Major growing regions in the country for wheat, rice,
cotton, groundnut, rapeseed/mustard and Rabi (winter) sorghum were covered. Production forecasts were made about a
month before the harvesting using multi-band remote sensing data acquired at optimum bio-window and weather data.
Ministry of Agriculture, satisfied with the performance of CAPE, came out with a request to target multiple crop
production forecasts starting with crop sowing to end of season.
Crop identification with remote sensing data requires using the data when crop has sufficiently grown.
However, forecasting of crop at sowing stage would require use of weather data and information on economic factors
controlling the farmer's response. Considering these things "Forecasting Agricultural output using Space, Agrometeorological
and Land based observations (FASAL)" concept was devised. FASAL aims at using econometric models
to forecast the area and production before the crop sowing operations. In unirrigated areas, information on amount and
distribution of rainfall is being used for forecasting the crop acreage as well as yield. Remote sensing data, both optical
and microwave form the core of crop area enumeration, crop condition assessment and production forecasting. Temporal
remote sensing data is being used to monitor the crop through its growing period. Vegetation indices and weather
parameter derived from surface and satellite observations will be used to develop the crop growth monitoring system.
Components of FASAL concept have been developed, tested and implemented through a series of exercise and
these are i) National wheat and winter potato production forecasting using IRS AWiFS data, ii) National Kharif rice
production forecasting using Radarsat SAR data, and iii) District level FASAL implementation in Orissa state. Typically
three in-season forecasts are being made. With this the FASAL concept of using the multi source data and techniques has
been successfully demonstrated.
FASAL implementation has been taken up to make national level multiple forecast of crops like rice, wheat, cotton,
sugarcane, rapeseed/mustard, rabi-sorghum, winter-potato and jute. Procedure development for use of remote sensing,
weather data - surface measurements as well as derived from satellite data, field and ancillary data to run the crop growth
simulation models has been taken up. The programme is sponsored by Ministry of Agriculture, Government of India.
Space Applications Centre of Indian Space Research Organisation has provided the scientific leadership to the project. A
large team drawn form a number of institutions such as ISRO/Department of Space, State Remote Sensing Applications
Centres, State Agricultural Universities and many other institutions are working for the project.
Multiple production forecasts of wheat in India using remote sensing and weather data
Author(s):
Markand P Oza;
Dhaniram Rajak;
Nita Bhagia;
Sujay Dutta;
Sarweshwar P. Vyas;
Naranbhai K. Patel;
Jai Singh Parihar
Show Abstract
This paper describes the methodology adopted and results obtained during forecasting of national level wheat
production in India using multi-date medium resolution Advanced Wide Field Sensor (AWiFS) data. Multidate,
geometrically registered and radiometrically normalized Resourcesat-1 AWiFS data were classified
using hierarchical decision rules, which exploited differential crop spectral profiles of various crops in winter
season. Wheat acreage estimates were arrived by aggregation of stratified samples. Wheat yields were
predicted for meteorological sub-divisions by correlation weighted weather regression models developed
using fortnightly temperatures. Multiple preharvest wheat acreage and production forecasts were made with
additional information on spatio-temporal crop growth performance in comparison to previous / normal
season.
Monitoring of wet season rice crop at state and national level in India using multidate synthetic aperture radar data
Author(s):
Manab Chakraborty;
Chakrapani Patnaik;
Sushma Panigrahy;
Jai Singh Parihar
Show Abstract
Rice crop grown during the monsoon (wet) season is the most important food grain in India. The crop is grown under
varied cultural and management practices. The present paper highlights the results of rice monitoring being carried out
for the past five years (2001-02 to 2005-06) using multi-date RADARSAT ScanSAR Narrow-B data. 30 ScanSAR
scenes covering thirteen states account for 95 percent of national crop area. 90 scenes are analysed to assess the national
wet season rice crop. A stratified sampling plan is used to analyse 5*5 km segments accounting for 15 per cent of the
crop area in each of the study states. A decision-rule classifier has been developed based on a Radiative Transfer (RT)
model developed and calibrated using large number of rice sites in India and controlled field experiments. This
procedure accounts for change in backscatter as a result of transplanting of rice and crop growth in multi-date data to
classify rice areas. Results indicate more than 93 per cent accuracy of area estimation at state level and 97 per cent at
national level. It is feasible to assess deviations in crop planting operation (late or early) for a given area.
Determination for regional differences of agriculture using satellite data
Author(s):
G. Saito
Show Abstract
Remote Sensing Laboratory, Field Science Center, Graduate School of Agriculture Science, Tohoku University starts at
April 2004. For studies and education at the laboratory we are now developing the system of remote sensing and GIS.
Earth Remote Sensing Data Analysis Center (ERSDAC) made the Home Pages of Terra/ASTER Image Web Library 3
"The Major Airport of the World." http://www.Ersdac.or.jp/ASTERimage3/library_E.html. First, we check the Airport
Data to use agricultural understanding for the world. Almost major airport is located in rural area and surrounded with
agriculture field. To survey the agriculture field adjacent to the major airport has almost the same condition of human
activities. The images are same size and display about 18km X 14km. We can easily understand field size and
surrounding conditions. We study seven airports as follows, 1. Tokyo Narita Airport (NRT), Japan, 2. Taipei Chiang kai
Shek International Airport (TPE), Taiwan, 3. Bangkok International Airport (BKK), Thailand, 4. Riyadh King Khalid
International Airport (RUH), Saudi Arabia, 5. Charles de Gaulle Airport (CDG), Paris, France, 6. Vienna International
Airport (VIE), Austria, 7. Denver International Airport (DEN), CO, USA. At the area of Tokyo Narita Airport, there are
many golf courses, big urban area and small size of agricultural fields. At Taipei Airport area are almost same as Tokyo
Narita Airport area and there are many ponds for irrigations. Bangkok Airport area also has golf courses and many ponds
for irrigation water. Riyadh Airport area is quite different from others, and there are large bare soils and small agriculture
fields with irrigation and circle shape. Paris Airport area and Vienna Airport area are almost agricultural fields and there
are vegetated field and bare soil fields because of crop rotation. Denver Airport area consists of almost agriculture fields
and each field size is very large. The advantages of ASTER data are as follows, 1. High-resolution and large swath, 2.
Large wavelength and many bands, 3. High-revel of geographical location, 4. Stereo pair images, 5. High performance
data searching system, 6. High speed data delivery system, 7. Cheap price, 8. Seven years observation and large volume
archive. A kind of project "Determination of Local Characteristics at Global Agriculture Using Archive ASTER Data"
was started at middle of November 2005. We establish data processing system and get some results. Paddy rice fields
analysis was started at first, we analyze 1) the Shonai Plains in Japan, 2) the Yangtze River delta in Middle-East China,
3) Mekong Delta in South Vietnam, 4) North-east Thai Plaines, Thailand, 5) Sacrament Valley, California, USA. The
results of this studies are as follows, 1) Using ASTER images, we can easily understand agricultural characteristics of
each local area. 2) ASTER data are high accuracy for location, and the accuracy is suitable for global study without the
fine topographical maps, 3) By five years observation of ASTER, there is huge numbers of ASTER scenes, but not
enough volumes for cloud free data for seasonal analysis. It means that follow-on program of ASTER is necessary, 4)
We need not only paddy field, but also all crop fields and all area, 5) The studies are necessary to international
corroboration.
Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs
Author(s):
Naveen Kalra;
P. K. Aggarwal;
A. K. Singh;
V. K. Dadhwal;
V. K. Sehgal;
R. C. Harith;
S. K. Sharma
Show Abstract
Wheat is an important food crop of the country. Its productivity lies in a very wide
range due to diverse bio-physical and socio-economic conditions in the growing regions.
Crop cutting and sample surveys are time consuming as well tedious, and procedure of
forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and
prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC
Research Programme, possibility of linking the dynamic wheat growth model with the
remote sensing input and other relational database layers was tried. Use of WTGROWS, a
wheat growth model developed at IARI, with the remote sensing and relational databases
is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs
are received. National wheat yield forecast was done for three seasons on meteorological
sub-division scale by using WTGROWS, relational database layers and satellite image.
WTGROWS was run for historic weather dataset (last 25 years), with the relational
database inputs through their associated growth rates and compared with the productivity
trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained
to capture the other biotic and abiotic stresses and subsequently used to bring down the
yields at each sub-division to realistic scale. The satellite image was used to compute the
acreage with wheat in each sub-division. Meteorological data for each-subdivision was
obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was
prepared. This was updated on weekly basis, and the methodology could forecast the
wheat yield well in advance with a great accuracy. This procedure shows the pathway for
Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning
and agri-production estimates, which although looks difficult for diverse agro-ecologies and
wide range of bio-physical and socio-economic characters contributing to differential
productivity trends.
Yield estimation in farmers' fields at Alipur Block of Delhi by using crop model and satellite data
Author(s):
V. K. Sehgal;
Naveen Kalra;
A. K. Singh;
V. K. Dadhwal;
R. C. Harith;
R. N. Sahoo;
S. K. Sharma
Show Abstract
A study was undertaken to validate the Wheat Growth Simulator (WTGROWS) in the farmers' fields of Alipur Block of Delhi and linking satellite derived vegetation index with the simulation model to estimate the wheat yield. Date of sowing, management practices and cultivars varied widely among the study sites. Leaf area index (LAI), phenological development and agronomic management (fertilizers and irrigation) were monitored at regular intervals for the 25 field sites selected in the study area. Above ground biomass and grain yield were recorded at harvest. Using the parameters derived for these sites, WTGROWS was run for each of the individual 25 sites. Crop phenology, temporal course of LAI and grain yield of each site was compared with the actual observations. The simulated and actual LAI temporal profile matched well for sites with different dates of sowing, excepting larger deviation noticed in the later stages of the crop growth. The simulated pre-anthesis duration and total above ground biomass were also correlated well with the observed values being mostly within ±15%. There were large discrepancies in simulated and observed grain yield. A satellite image near anthesis of IRS 1D LISS-3 was acquired for the study area. The sites were identified on the image and their vegetation indices were derived. Average grey value in Infrared (IR) and Red (R) band, Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) were giving significant relation with measured LAI of 5th February which corresponded to crop anthesis stage. The relation between vegetation indices and LAI was logarithmic in nature. This logarithmic relation was incorporated into the WTGROWS to force the LAI to the equation-derived value at particular growth stage and model yield was computed and compared with actual observations.
Identification of sugarcane and onion crops using digital image processing of multidate multisensor high-resolution satellite data
Author(s):
Dhananjay S. Pandit;
Yelisetti V. N. Krishna Murthy;
V. Jayaraman
Show Abstract
Timely assessment of area under different standing crops and their spatial distribution is essential for irrigation planning
and management in command areas. The conventional methods of collecting information on crop acreage are time-consuming
and uneconomical, especially when large areas are involved. The modern Remote Sensing technology
provides real-time, accurate and cost-effective data on crop acreage due to its multi-spatial, multi-spectral and multi-temporal
nature. The attempt has been made in this study to use the multi-date data of RESOURCESAT sensors like
AWiFS, LISS-III and LISS-IV as well as multispectral data of IKONOS with suitable digital imaging processing
techniques to identify Sugarcane, which is a long duration crop and Onion, which is a short duration crop, in order to
plan need based irrigation facilities, to collect the revenue from the farmers in the form of water assessment, better crop
management and future crop planning.
Sampling approach for estimation of crop acreage under cloud cover satellite data in hilly regions
Author(s):
Prachi Misra Sahoo;
Anil Rai;
Sudhakar Krishnamoorthy;
B. K. Handique;
P. P. N. Rao;
M. P. Oza;
J. S. Parihar
Show Abstract
Crop acreage estimation in hilly regions is till date a challenge for the remote sensing community due to the problems of undulating topography, inaccessibility to vast areas, smaller field size, practice of shifting cultivation, accounting for area falling under hill shades and valleys. Remote sensing alone may not be able to provide reliable estimate of crop acreage in these areas. In addition to this if these regions are humid or tropical for which it is difficult to get cloud free data then the problem becomes even more complicated. Many studies has been conducted in past for cloud/shadow removal but to estimate the crop area under the cloud cover has not been attempted. In this study at attempt has been made to estimate area under paddy crop in a district of Meghalaya by using sampling approach devised by integrating remote sensing, GIS and ground survey data taking into account the problem of hilly regions mentioned above. In addition to this technique for estimating the area under cloud /shadow using the previous year data and only on the basis of current year data is also proposed.
Use of a root zone soil moisture model and crop spectral characteristics to estimate sorghum yields in a dryland Alfisol toposequence
Author(s):
Uttam Kumar Mandal;
U. S. Victor;
N. N. Srivastava;
K. L. Sharma;
V. Ramesh;
M. Vanaja;
G. R. Korwar;
Y. S. Ramakrishna
Show Abstract
This study investigated the relationship between sorghum grain yield over range of soil depth with seasonal crop water stress index based on relative evapotranspiration deficits and spectral vegetation indices. A root zone soil moisture model has been used to evaluate the seasonal soil moisture fluctuation and actual evapotranspiration within a toposequence having varying soil depth of 30 to 75 cm as well as different available water capacity ranging from 6.9% to 12.6% (V/V%). The higher r2 values between modeled and observed values of soil water (r2> 0.69 significant at <0.001) and runoff (r2 = 0.95, significant at P<0.001) indicated good agreement between model output and observed values. The spectral vegetation indices like simple ratio, normalized difference vegetation index (NDVI), green NDVI, perpendicular vegetation index, soil adjusted vegetation index (SAVI) and modified SAVI (MSAVI) was recorded through out the growth period of sorghum. The vegetation indices except perpendicular vegetation index measured during booting to anthesis stages were positively correlated (P<0.05) with leaf area index and yield. The MSAVI measured during booting to milk-grain stage have the highest positive correlation with yield. Variation was noticed when additive and multiplicative forms of water-production functions calculated from water budget model were used to predict crop yield. But the yield estimation was improved when spectral vegetation indices measured during booting to milk-grain is incorporated along with water production functions. The water budget model along with spectral vegetation indices gave satisfactory estimates of sorghum grain yields and appears to be a useful tool to estimate yield as a function of soil depth and available soil water.
Quantitative retrieval of crop water content under different soil moistures levels
Author(s):
Jiahua Zhang;
Wenjuan Guo
Show Abstract
The characteristics of canopy spectrum and growth status of winter wheat under different soil moisture levels were studied in the field. Correlations between FMC and EWT of leaf and spectral reflectance of canopy were calculated and analysed quantitatively, and the sensitive bands to leaf water were found. Simple ratio water index(SWI)and normalized difference water index(NDWI) were constructed with the sensitive bands. Simple statistical models at different growth stages were established using spectral indices data and FMC and EWT of leaf. Bands centered at 469, 645, 700 and 710nm of VIS region, bands centered at 760, 815, 855, 930, 1075, 1100nm of NIR region and bands centred 550, 1600, 1640, 1750, 2130nm of SWIR were defined as sensitive bands to estimate leaf water content. These bands centered atmosphere windows had the potential to be applied in monitoring canopy leaf content of crop. The SWI and NDWI constructed with the sensitive bands could estimate leaf content more accurately than single band. The four band MODIS combined index: R (1640,2130) / ND (855,555) showed a good indicator to detect canopy water content of winter wheat.
Retrieval of plant biophysical parameters through inversion of PROSAIL model
Author(s):
Rahul Tripathi;
R. N. Sahoo;
V. K. Sehgal;
R. K. Tomar;
S. Pandey;
D. Chakraborty;
N. Kalra
Show Abstract
Directional reflectance measurement has been found to be better and more reliable compared to the conventional statistical approach to retrieve plant biophysical parameters as it takes care of its anisotropic nature. Keeping this in view, a field experiment was conducted with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments to generate a wide range of Leaf Area Index (LAI) and biomass. The reflectance data obtained at 5nm interval for a range of 400- 1100nm were integrated to IRS LISS -II sensor's four band values using Newton Cotes Integration technique. Biophysical parameters were estimated synchronizing with the bi-directional reflectance measurements. The radiative transfer model PROSAIL was used for its evaluation and to retrieve biophysical parameters mainly LAI and Average Leaf Angle (ALA) through its inversion. Look Up Table (LUT) of BRDF was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40 to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al., 1986. The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.
Spectral reflectance-based detection of nitrogen content in fresh tea leaves
Author(s):
Yongguang Hu;
Pingping Li;
Hanping Mao;
Bin Chen;
Yu Ding
Show Abstract
The method of visible and near infrared reflectance spectroscopy is adopted to analyze relative nitrogen content inside
fresh tea leaves because it is fast and non-destructive. The spectroradiometer, FieldSpec 3 is used to acquire the spectral
reflectance of fresh tea leaves in the field, and the relative value of nitrogen content inside fresh tea leaves are measured
with the chlorophyll meter, SPAD 502. About 150 leaves are sampled, which cover the wide range of relative nitrogen
content from 20 to 90. A software NIRSA is used to process the spectra data. The preprocessing of spectra includes the
second derivative of reflectance with the gap of 25 points and smoothing with Savitzky-Golay filter for removing spectra
noise. And the normalizing operation is not done because the variation of sunlight optical path could be ignored. 122
samples are used to establish the calibration model with the method of PLS regression, and multiple correlation
coefficient is 0.7301 and RMSEC is 10.0781. The prediction model is established with the prediction set made up of 13
samples and the correlation coefficient is 0.8992 and RMSEP is 10.698. Spectral reflectance-based detection of nitrogen
content or chlorophyll in fresh tea leaves is applicable.
Spectral characteristics of peanut crop infected by late leafspot disease under rainfed conditions
Author(s):
M. Prabhakar;
Y. G. Prasad;
U. K. Mandal;
Y. S. Ramakrishna;
C. Ramalakshmaiaha;
N. C. Venkateswarlu;
K. Subramanyam
Show Abstract
A leafspot susceptible peanut cultivar (cv JL 24) was sown during kharif season of 2005 in rainfed region of Andhra
Pradesh, India. Five disease levels (scale 0-4) were created in the field by differential fungicidal spray schedule, and each
treatment was replicated four times. Spectral data was recorded at 2 nm intervals using a portable spectroradiometer
within a spectral range of 300-1100 nm. The loss of leaf pigments (Chlorophyll a and b) due to diseases was quantified
using spectrophotometer. The red and infrared reflectance values between 620- 680 and 770-860 nm, respectively were
used for calculating Normalized Difference Vegetation Index (NDVI). Significant reduction in chlorophyll content was
observed only when the disease reached a stage of scale 2 and above. The ratio of chlorophyll a to b showed a declining
trend as the number of spots per leaf increased. From the spectral reflectance studies typical chlorophyll absorption
bands (350 - 500 and 620-690 nm) of healthy and diseased plants could not be differentiated. However, the difference
was evident in the chlorophyll reflection bands (522- 600 nm). The infrared spectral region between 700-850 nm was
found to be sensitive to canopy disease stress. The reflectance (%) in this part of spectrum was higher for healthy plants
compared to diseased plants. But, the low level of disease intensity (scale 1) was not differentiated by the spectral
reflectance. The NDVI value for 82 days old healthy crop was in the range of 0.45 to 0.50, while the same for diseased
plants was between 0.34 and 0.45. This study finds potential application of remote sensing techniques in detection of
plant diseases.
Estimating actual evapotranspiration using RS and GIS
Author(s):
Vajja Hari Prasad;
R. Hrishikesh Mahadev
Show Abstract
Evapotranspiration, a major component in terrestrial water balance and net primary productivity models, is difficult to
measure and predict. Remote sensing cannot provide a direct measurement of evapotranspiration (ET) but it can provide
a reasonably good estimate of Evaporative Fraction (EF), defined as the ratio of ET and available energy. Recent studies
have successfully estimated EF using a contextual interpretation of radiometric surface temperature (T0) and normalized
difference vegetation index (NDVI) such as from the Advanced Very High Resolution Radiometer (AVHRR) onboard
NOAA-14 satellite and Moderate Resolution Imaging Spectroradiometer (MODIS), onboard EOS Terra satellite,
sensors to estimate ET over large areas. This study compares AET from MODIS and AVHRR applied to a mountainous
river basin in Indian Himalayas. Surface Energy Balance (SEBAL) has been used for estimating Actual
Evapotranspiration (AET) in the region. In this paper, remote sensing data are used to evaluate the surface albedo, net
radiation, ground heat flux, sensible to estimate evapotranspiration and surface conditions using energy balance
approach. Being a mountainous basin, an attempt has been made to consider terrain effects in estimating net radiations.
Results showed that AVHRR gives an average value whereas MODIS data gives better results since resolution of
MODIS is better than that of AVHRR.
Mapping land degradation and desertification using remote sensing data
Author(s):
S. K. Saha;
Munish Kumar;
Bhajan Lal;
Alok Kumar Barman;
Satyendra Nath Das
Show Abstract
Land degradation is the result of both natural and biotic forces operating on the earth. Natural calamities, over exploitation of land resources, unwise land use and the consequences of high inputs agriculture on soil and water resource are of great concern both at national and international level. It aggravated food insecurity in the world especially in the developing countries that calls for combating land degradation and desertification with scientific means. Development of degraded lands in India is one of the options to enhance food production and to restore the fragile ecosystem.
The scientific information and spatial distribution of various kinds of degraded lands is thus essential for formulation of strategic plan to arrest the menace of land degradation. Remote sensing provides an opportunity for rapid inventorying of degraded lands to generate realistic database by virtue of multi-spectral and multi-temporal capabilities in the country. The satellite data provides subtle manifestations of degradation of land due to water and wind erosion, water-logging, salinity and alkalinity, shifting cultivation, etc., that facilitate mapping.
All India Soil and Land Use Survey (AISLUS) has undertaken the task of land degradation mapping using remotely sensed data and developed a methodology accordingly. The mapping has been conceptualized as a four-tier approach comprising kind of degradation, severity of degradation, degradation under major landform and major land use.
Visual mode of interpretation technique based on image characteristics followed by ground verification has been employed for mapping of degraded lands. Image interpretation key has been formulated based on the spectral signatures of various causative factors of different kinds of degraded lands. The mapping legend has been made systematic and connotative. The extent and spatial distribution of different kinds of degraded lands with degree of severity under major landform and major land use in a district could be derived easily from the report published by the organization.
Generation of realistic information on degraded lands of the country is utmost necessary. It should be given due importance and taken up on mission mode in order to check further degradation of the environment and loss of top fertile soils. The data base would enable to develop District Information System using advanced technology for periodic monitoring and development of degraded lands.
Analysis of cropping pattern and crop rotation using multidate, multisensor, and multiscale remote sensing data: case study for the state of West Bengal, India
Author(s):
K. R. Manjunath;
Nitai Kundu;
Sushma Panigrahy
Show Abstract
The repetitive cultivation of an ordered succession of crops (or crop and fallow) on the same land defined as crop
rotation has a significant role on sustainability of agricultural practice. This paper highlights the methodology used to
map seasonal cropping pattern and crop rotation of West Bengal state in India. Multi-date, remote sensing data of IRS
WiFS and Radarsat SAR were used to map seasonal cropping patterns, which were combined to derive the crop rotation
map. Three distinct crop-growing seasons could be identified. The main one coinciding with monsoon from June-
October, followed by winter crop season from November- February and the summer one March-June. It was feasible to
classify seven major crops using the SAR and WiFS data sets. Rice is the dominant crop in wet season occupying more
than 75 per cent of net sown area. Mustard, potato, wheat, gram, rice are the major dry season crops. Rice-rice, ricepotato,
rice-wheat, rice-mustard, rice-gram, and jute-rice were the major two crop rotations. Rice-fallow was the
dominant practice accounting for 55 per cent of area.
Performance of different vegetation indices in assessing degradation of community grazing lands in Indian arid zone
Author(s):
Suresh Kumar;
Gary Bastin;
Margaret Friedel;
Pratap Narain;
D. K. Saha;
U. R. Ahuja;
B. K. Mathur
Show Abstract
Vegetation in arid community grazinglands shows monsoonal growth. Its matching phenology with crops
makes its detection difficult during July to September. While crops are harvested during September-October, using
satellite data thereafter for the natural vegetation seems most appropriate but by then it turns dry. An index capable of
sensing dry vegetation was needed since conventional NDVI is sensitive to greenness of vegetation. Performance of
NDVI vis-à-vis another index, PD54, based on cover was therefore compared in assessing degradation of grazinglands.
The PD54 was used to isolate anthropogenic impacts from environmental induced degradation by analyzing satellite
images from dry and wet seasons. Substantial absence of appreciable vegetation response indicated poor resilience and
severe degradation.
Five grazinglands in Shergarh tehsil of Jodhpur district in Rajasthan were studied following above approach.
Ground radiometric observations were recorded. Satellite data of IRS 1C/1D/P6 with LISS 3 sensor for both pre and
post monsoon season were acquired for three contrasting wet-dry season events.
These were geometrically registered and radiometrically calibrated to calculate an index of vegetation cover
PD54 as well as NDVI. PD54 is a perpendicular vegetation index based on the green and red spectral band width. The
PD54 and NDVI calculated from spectro-radiometer were related to vegetation cover measured on ground in permanent
plots. This confirmed that PD54 was superior index for estimating cover in arid dry grasslands.
These ground vegetation trends in a good rainfall year (2001) with drought year (2002) were related with
satellite data for a protected and four unprotected grazinglands. NDVI failed to detect any vegetation in protected areas
supporting excellent grass cover which was succinctly brought out by PD54. Successful validation of PD54 in detecting
degradation of 13 additional sites confirmed its efficacy. These findings have implication in forage availability
assessments, forage forecasting, drought preparedness, pastoralism and transhumance.
Remote sensing and GIS based information system for sustainable resources planning at Panchayat level
Author(s):
A. Velmurugan;
S. Bhatt;
V. K. Dadhwal
Show Abstract
Spatial databases of natural resources are very much essential to ensure enhanced productivity by conserving
soil and water and to maintain ecological integrity of any region. Integration of various thematic layers prepared from
high resolution data and detailed field survey would be preferred for grass root level planning (Panchayat) aimed to
realize the potential of production system on a sustained basis. In this study, a detailed spatial data base was created for
part of Kasaragod dist., Kerala, India. Detailed soil survey was carried out using cadastral map and registered over high
resolution satellite data (IRS LISS-IV) which helped to identify problems and potentials of the area. Nearly 600 ha of
land were found to be at higher erosion risk category out of ten soil series identified in the study area. Remote sensing
data was used to prepare land use/land cover map and coconut (53%) followed by mixed vegetation type (16%) were
found to be dominant. Soil site suitability assessment for major crops of the area was carried out and crossed with
present land use to get the mismatch in land use/land utilization type. Alternate land use plan was prepared considering
the potentials and problems of various available resources. Decision Support System (DSS) along with user interface is
developed to support decision and extract relevant information. As organic carbon is one of the most important
indicators of soil fertility C stock in the present and proposed land use was also estimated to understand the
environmental significance.
Soil moisture estimation from ERS-2 SAR data in Solani River catchment
Author(s):
S. Said;
U. C. Kothyari;
M. K. Arora
Show Abstract
Backscatter coefficient estimated from ERS-2 SAR sensor can effectively be used to derive soil moisture state
of a river catchment which is of great importance from hydrological point of view. However, the backscatter coefficient
is highly affected by a number of factors such as topography, vegetation density, and variations in small-scale surface
roughness. Analysing the effect of these factors to eliminate their effect on backscatter coefficient for accurately
estimating the soil moisture is the main focus of the present study. ERS-2 SAR image of date 28th July 2003 (i.e., autumn
season) was utilised for carrying out the study in a typical river catchment in India. Incidence angle based model was
used to account the effects due to topography. The effects of vegetation on backscatter coefficient were minimised by
using the semi-empirical water cloud model. Four agricultural crops and grassland compose the set of vegetation classes
in the study area. A comparative study between three important parameters that describe vegetation in terms of their bulk
characteristics (e.g., leaf area index; LAI, plant water content; PWC and crop height 'h') was carried out to identify a
vegetation descriptor that had the maximum influence on backscatter coefficient. The effect of three canopy descriptors
namely LAI, PWC and h were assessed on individual basis by proposing three separate models used in the water cloud
model so as to simplify the model, that could use a single canopy descriptor instead of two or more as used in many other
studies. Results indicated that the backscatter coefficient obtained from the model using LAI showed stronger
relationship with the observed volumetric soil moisture with high R2 values. A nonlinear least square method (LSM) was
implemented to estimate volumetric soil moisture. A significantly high correlation was obtained between the retrieved
soil moisture and the observed soil moisture with high R2 values of the order of 0.95 to 0.97 and low rmse values for
almost all the vegetation classes and barren land. Subsequently, soil moisture map of the study area was generated from
the SAR image.
Programs for Watershed-Plus phase for rainfed regions in India
Author(s):
Kausalya Ramachandran;
Y. S. Ramakrishna
Show Abstract
Watershed-based development is the strategy for sustainable growth in the vast rain-fed regions of India since 1980s to enhance agricultural production, conservation of natural resources and raising rural livelihood of farming communities. Although soil and water conservation was initially the primary objective of watershed program that saw large public investment since inception, later its focus shifted to principles of equity and enhancing rural livelihood opportunities and more recently to sustainable development since mid-1990s. At present a major emphasis under watershed program is the regeneration of degraded fragile lands in rain-fed regions. Several noteworthy watershed programs have been carried out since inception that have yielded sterling results while many others have yielded little by way of unbalanced development because of improper characterization of watersheds and poor project planning and implementation. Tools of Geomatics like satellite data, GIS and GPS besides conventional ones like field survey, topographical and cadastral maps along with traditional multi-disciplinary methods like PRA, soil and water analysis, socio-economic survey etc. provide insight into characterization of watersheds, project formulation and proper implementation of such development programs. The present paper illustrates the methodology for characterization of watersheds using the tools of Geomatics on one hand, besides exhibiting its utility for scaling-out the program benefits like sustaining higher agricultural productivity, enhancing irrigation efficiency, equity, enhanced rural livelihood opportunities, women empowerment, drought-proofing etc. during Watershed-Plus phase in the coming decades, on the other.
Comparative evaluation of various algorithms for drainage extraction using Cartosat-1 stereo data
Author(s):
Ritesh Agrawal;
Nadeem Ahmad;
Jayaprasad P;
Anjum Mahtab;
J. A. Vinoth Kumar;
S. K. Pathan;
Ajai
Show Abstract
Hydrological parameters have vital role in civil engineering, infrastructure planning and
natural resource management. Since the last one and a half-decade their extraction using
the High-resolution satellite stereo data has been very helping and less time consuming.
In the earlier times, the ground surveys use to take a lot of time to cover a small area.
Whereas with the availability of high accuracy Digital Elevation models from satellite
stereo data one can extract these parameters with ease and good accuracy. During last
one and half-decade vast experience has been gained in techniques of processing stereo
data acquired from space. This paper deals with the extraction and validation of
hydrological parameters from digital elevation model (DEM) derived using CARTOSAT-1
stereo data. In this paper two sub-watershed, one in Chamoli, Uttaranchal and other in
Shimla, Himachal Pradesh were selected, A detailed morphometric analysis of the derived
drainage network from the various sources and using various algorithms has been carried
out. The parameters like stream order, stream length, bifurcation ratio, length ratio etc.,
have been used for comparison purpose.
Digital spatial soil and land information for agriculture development
Author(s):
R. K. Sharma;
Pankaj Laghathe;
Ranglal Meena;
Alok Kumar Barman;
Satyendra Nath Das
Show Abstract
Natural resource management calls for study of natural system prevailing in the country. In
India floods and droughts visit regularly, causing extensive damages of natural wealth including
agriculture that are crucial for sustenance of economic growth. The Indian Sub-continent drained by
many major rivers and their tributaries where watershed, the hydrological unit forms a natural
system that allows management and development of land resources following natural harmony.
Acquisition of various kinds and levels of soil and land characteristics using both
conventional and remote sensing techniques and subsequent development of digital spatial data base
are essential to evolve strategy for planning watershed development programmes, their monitoring
and impact evaluation. The multi-temporal capability of remote sensing sensors helps to update the
existing data base which are of dynamic in nature.
The paper outlines the concept of spatial data base development, generation using remote
sensing techniques, designing of data structure, standardization and integration with watershed
layers and various non spatial attribute data for various applications covering watershed
development planning, alternate land use planning, soil and water conservation, diversified
agriculture practices, generation of soil health card, soil and land reclamation, etc. The soil and land
characteristics are vital to derive various interpretative groupings or master table that helps to
generate the desired level of information of various clients using the GIS platform.
The digital spatial data base on soils and watersheds generated by All India Soil and Land
Use Survey will act as a sub-server of the main GIS based Web Server being hoisted by the
planning commission for application of spatial data for planning purposes under G2G domain. It
will facilitate e-governance for natural resource management using modern technology.
Effect of spatial resolution on watershed characteristics and the ANSWERS model hydrological simulations for a small watershed
Author(s):
Ramadhar Singh;
Kamlesh Narayan Tiwari;
Bimal Chandra Mal
Show Abstract
The present study was undertaken to investigate the effect of cell size variation on watershed characteristics and
hydrological simulations of the physically based distributed parameter Areal Non point Source Watershed Environment
Response Simulation (ANSWERS) model. The study is carried out in Banha watershed located in Upper Damodar
catchment, Jharkhand, India having 16.13 km2 area (with average slope of 1.91%.) using Digital Elevation Model
(DEM), GIS and remote sensing techniques for automatic extraction of the model input parameters. The spatial
resolution (cell size) variation from 30m to 150m with incremental step of 30m influences the accuracy of watershed
characteristics extracted from DEM. The flow path length and average watershed slope decreased by 53.71% and
20.94% respectively due to variation in cell size. Important watershed parameters such as drainage area, stream network,
slope etc. were extracted most accurately automatically with variations less than 10% using DEM of 30m resolution
through EASI/PACE and IDRISI GIS. Land use and land cover information generated from Indian Remote Sensing
Satellite (IRS-1B, LISS-II) data at 30 m resolution resulted in overall classification accuracy greater than 88%. The
watershed hydrological data from fifteen storms of 1995 and 1996 were used for the ANSWERS model cell size
sensitivity study. The runoff, peak flow and sediment yield simulations by the model decrease as cell size increases from
30 m to 150 m. The model simulated peak flow at acceptable accuracy for 30 m cell size. The runoff and sediment yield
simulations are not observed to be significantly different from the observed values up to 120 m cell size.
Modeling of flood events using spatially distributed unit hydrograph
Author(s):
S. Saravanan;
Z. Ahmed;
Manoj K. Jain
Show Abstract
In this study surface runoff resulting from isolated rainfall events has been estimated using spatially distributed unit hydrograph. The digital elevation model (DEM) was prepared from the contour map of watershed and used to extract the cell to cell flow path, flow direction, flow length, and slope. Soil Conservation Service-Curve Number (SCS-CN) method was used to compute the excess rainfall generated from each cells. The surface runoff generated from each cell was routed to the watershed outlet through the stream network. Kinematic wave equation was used to calculate the overland flow travel time of each cell in the watershed. The watershed Time Area isochrone was developed by summing up the time of concentration in each cell. S-hydrograph technique has been used to derive spatially distributed unit hydrographs. The distributed unit hydrograph has been convoluted with one hour rainfall excess hyetograph to get the direct runoff hydrograph for the storm event. The developed model was applied to five storm events and observed that the derived flood hydrograph of this model was closely matching with the flow records of the watershed. Indicating suitability of present modeling approach for predicting the direct runoff for given storm event of rainfall.
Effect of contamination and mixed objects on snow reflectance using spectroradiometer in Beas Basin, India
Author(s):
H. S. Negi;
A. V. Kulkarni;
S. K. Singh
Show Abstract
Field observations were carried out using Spectroradiometer (350-2500nm) to understand effect of
contamination on the snow reflectance and mix of snow, vegetation and soil by different proportions on its reflectance.
The experiments were carried out in Beas Basin, India during the winter 2004-05 and 2005-06. The investigation has
shown that as contamination increases, snow reflectance decreases in all wavelengths, however highest decrease was
observed in visible region. In addition, peak of reflectance shifted to high wavelengths, as amount contamination
increases. For snow cover and soil cover mix, as proportion of soil cover increases reflectance increases in SWIR region
and decreases in visible region. In snow and vegetation mix, as proportion of vegetation increases, curve pattern of
vegetation was observed. This means reflectance and absorption due to vegetation presence in visible and NIR region
can be observed. If proportion of vegetation area is equal to snow, then reflectance pattern is similar to vegetation.
However, if vegetation proportion is lower, then reflectance curve moves to higher reflectance range in visible and NIR
region and beyond that reflectance slightly reduces. This study suggests, if hyperspectral data is available then proportion
of snow in mixed pixel can be estimated. This study will help in improving existing Normalized Difference Snow Index
based algorithms for snow cover monitoring and developing new algorithm, if hyperspectral data is available.
Glacial retreat in Himalaya using Indian remote sensing satellite data
Author(s):
Anil V. Kulkarni
Show Abstract
Himalayas possess one of the largest resources of snow and ice, which act as a huge freshwater reservoir. Monitoring the
glaciers is important to assess the overall reservoir health. In this investigation glacial retreat was estimated for 466
glaciers in Chenab, Parbati and Baspa basins from 1962. Expeditions to Chhota Shigri, Patsio and Samudra Tapu glaciers
in Chenab basin, Parbati glacier in Parbati basin and Shaune Garang glacier in Baspa basin were organized to identify
and map glacial terminus. The investigation has shown, an overall reduction in glacier area from 2077 sq km to 1628 sq
km from 1962, an overall deglaciation of 21 percent. However, number of glaciers is increased due to fragmentation.
Mean of glacial extent was reduced from 1.4 to 0.32 km2 between 1962 and 2001. In addition, number of glaciers with
higher areal extent is reduced and lower areal extent has been increased between the periods. Small glaciarates and ice
fields have shown extensive deglaciation. For example, 127 glaciarates and ice fields less than 1 km2 have shown retreat of 38 percent from 1962, possibly due to small response time. This means combination glacial fragmentation, higher
retreat of small glaciers and climate change are influencing sustainability of Himalayan glaciers.
An automatic segmentation method for multi-tomatoes image under complicated natural background
Author(s):
Jianjun Yin;
Hanping Mao;
Yongguang Hu;
Xinzhong Wang;
Shuren Chen
Show Abstract
It is a fundamental work to realize intelligent fruit-picking that mature fruits are distinguished from complicated backgrounds and determined their three-dimensional location. Various methods for fruit identification can be found from the literatures. However, surprisingly little attention has been paid to image segmentation of multi-fruits which growth states are separated, connected, overlapped and partially covered by branches and leaves of plant under the natural illumination condition. In this paper we present an automatic segmentation method that comprises of three main steps. Firstly, Red and Green component image are extracted from RGB color image, and Green component subtracted from Red component gives RG of chromatic aberration gray-level image. Gray-level value between objects and background has obviously difference in RG image. By the feature, Ostu's threshold method is applied to do adaptive RG image segmentation. And then, marker-controlled watershed segmentation based on morphological grayscale reconstruction is applied into Red component image to search boundary of connected or overlapped tomatoes. Finally, intersection operation is done by operation results of above two steps to get binary image of final segmentation. The tests show that the automatic segmentation method has satisfactory effect upon multi-tomatoes image of various growth states under the natural illumination condition. Meanwhile, it has very robust for different maturity of multi-tomatoes image.
On the optimal choice of parameters in using fuzzy clustering algorithm for segmentation of plant disease leaf images
Author(s):
Yancheng Zhang;
Hanping Mao;
Yongguang Hu;
Bo Hu
Show Abstract
As an important classifier, fuzzy c-means clustering technique has been widely used in segmentation of image. It is an
adaptive segmentation method for plant disease images. However, it has some uncertain factors, when it is used for
specific segmentation problem, that are input parameters value. The input parameters include the feature of the date set,
the optimal number of cluster, and the degree of fuzziness. These parameters affect the speed and precision of fuzzy
clustering segmentation. In this paper, the optimal choice of parameters in a fuzzy c-means algorithm for segmentation of
plant disease image was discussed and investigated. Using the pixels gray and means of neighborhood pixels as input
feature data; an adapting the FCM algorithm parameters based on fuzzy partition entropy, fuzzy partition coefficient, and
compactness measures was used to choose the optimal cluster number; and experiments was used for choosing the
degree of fuzziness. The Results show that the optimal clustering number for disease leaf segmentation problem is 4 and
the degree of fuzziness is 2.
Comparison of the extracted DEMs in Heihe River upper and middle reaches
Author(s):
Zhenhua Chao;
Zhuotong Nan;
Xin Li;
Zhihua Mao
Show Abstract
The Digital Elevation Model (DEM) are terrain elevations at regularly spaced horizontal intervals, i.e., an a
grid of regularly spaced elevations. With the development of computing technology, the methods of data
acquisition, data storage and data processing speed for DEMs get along well. Heihe river lies in the northwest
of China and is a continental river. It roots from snow-ice and disappears in deserts. The drainage area is about
140,000 square kilometers and the river is the main water source for the living. The upper reaches are
mountainous and the runoff is very important. The middle reaches are oasis. At present, many hydrological
and ecological models are introduced. The catchment basin and stream network data acquired from DEMs are
main input data for many surface hydrological models. So the quality and resolution of DEMs are significant.
Software ENVI Version 4.2 furnishes with the DEMs Extraction Module. The paper compared several
methods for extracting DEMs of the upper reaches. We extracted the elevation data from the ASTER---stereo
images with the module and created the DEM. Secondly; we collected the DEM based on the contour map of
Heihe river. The comparison of the DEMs quality was carried out between the DEM from the contour map,
the DEM extracted by ENVI Version 4.2. To a certain extent, the DEM from Aster imagery can reflect the
terrain and be used in hydrological models.
Incorporating remote sensing data in crop model to monitor crop growth and predict yield in regional area
Author(s):
Jianmao Guo;
Weisong Lu;
Guoping Zhang;
Yonglan Qian;
Qiang Yu;
Jiahua Zhang
Show Abstract
Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable
development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over
homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial
information about model inputs, such as the value of some of their parameters and initial conditions, which may have
great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By
incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote
sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate
new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the
use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop
growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield
significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions
to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote
sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.
Estimation of rice yield per unit using MODIS spectral index
Author(s):
Qian Cheng;
Xiuju Wu;
Zhihua Mao D.D.S.
Show Abstract
The first 19 bands of MODIS, covering visible to shortwave infrared spectral wavelength, were simulated by the
ground-level reflectance spectra. All Normalized Difference Vegetation Index (NDVI)-like and Ratio Vegetation Index
(RVI)-like spectral indices formed by every two bands were calculated to obtain their determinate coefficients with
theoretical and real yield. Results showed that combinative NIR-infrared index (b2, b19) and (b16, b19) of MODIS
were strongly correlated with rice yield, specially the correlative coefficient exceeded the significant level in maturing
stage, but combinative visible light index were strongly correlated with rice yield in early stage and poorly in late stage.
The best spectral index were the combination of (b2, b19) and (b16, b19) in predicting rice yield in whole rice growth, if
considering the spatial resolution, Enhanced Vegetation Index (EVI) was better than the two band combination of (b2,
b19) and (b16, b19) and was best suitable for monitoring rice yield.
Study on the hyperspectral characteristics of cotton
Author(s):
Xiuju Wu;
Qian Cheng;
Zhihua Mao
Show Abstract
Studies on the spectral reflectance of canopy and leaves of various cottons were performed. Spectral reflectance of six
variety cotton over the 350-2500 nm range with a spectral resolution of 3 nm were obtained this study. In the early
growth the spectral reflectance of cotton canopy increased with time, but in the later growth the spectral reflectance of
cotton canopy decreased with time. In the main stem of cotton, the spectral reflectance of leaf decreased with leaf
position up. The spectral reflectance of leaf increased with addition of leaves. The curve of spectral reflectance was up
with water losing of leaf. In the single leaf, the spectral reflectance in leafstalk was higher than the spectral reflectance
in edge of leaf. The action of derivative spectra on eliminating background influence on cotton canopy spectra and
that of vegetation indices on determining agricultural parameters were analyzed. The results show that the position and
slope of red edge of canopy spectral reflectance of cotton are certainly correlative to fractional vegetation cover.
Validation of MOD15-LAI and MOD13 using insitu rice data
Author(s):
Xiuju Wu;
Qian Cheng;
Zhihua Mao
Show Abstract
Before MODIS products will be applied in rice monitoring, it need to be validated. A site-intensive MODIS product
validation on rice planting region in southern China was performed. Validation approach involved scaling up
independent fine-grained datasets, including ground and high spatial resolution imagery (Thematic Mapper), to the
coarser MODIS spatial resolutions. Results showed that The difference between situ rice LAI and MOD15-LAI in
different rice growth sage is different. The differences are bigger in booting stage, heading stage and milking stage, but
smaller in tiller stage and maturing stage. The 16-day composited MODIS reflectance and VIs matched well with
ground measurement reflectance and VIs. Otherwise, results indicate that the MODIS algorithm will underestimate LAI
value by about 5-10 % in total from 1-km resolution data over the Situ rice LAI at Southern China, the mean and
standard deviation deviated from MOD15-LAI are smaller than that of situ rice LAI.
Sediment and nutrient loading from non-degraded and degraded watershed area in to a tropical water body: a case study using remote sensing
Author(s):
Mool Chandra Gupta
Show Abstract
The present study deals with the works relating to integrated watershed management on sustainable basis for evolving tractable operational package so that nutrient, sediment and runoff losses from catchment could be minimized.
Study area lies between latitudes 22°5' and 22°12' and longitudes 77°17' and 77°23' covering an area of 6357.5 hectares. Physically it is divided into two different parts, hills and plains. The height of elevation of study area is in between 518 to 630 meters above m.s.l. The thematic maps were generated using satellite data.
The present tropical catchment possessing diverse forest ecosystem and agriculture land characterized by weathered black cotton soil derived from basalt with the slope ranging from nearly level to moderately steep to steep sloping and receiving average annual rainfall 1150 mm.
The annual return of carbon and nutrient (N, P, K, Ca, Na and Mg) in non degraded and degraded forest and nutrient concentrations in runoff flow and sediment output (sediment loss) during monsoon period from non-degraded forest, degraded forest and agriculture lands were worked out.
The sediment and nutrient losses from the catchment to the tropical water body are alarming particularly from agricultural land. The nutrient losses in both the forms (runoff water plus sediment movement) are in the order of agriculture > degraded forest > non-degraded forest. The loss of soil in the form of sediment loss follows the same pattern. The results were alarming when the value of sediment loss of forest was compared to the agriculture land of the catchment. The soil loss as sediment is 33.5 times greater in agriculture land compared to non-degraded forest and 10.2 times greater in agriculture land compared to degraded forest.
For assessing yields under extreme climatic events using crop simulation models: aerosol layer effects on growth and yield of wheat, rice, and sugarcane
Author(s):
Naveen Kalra;
D. Chakraborty;
R. N. Sahoo;
V. K. Sehgal;
Manish Singh
Show Abstract
Aerosol presence reduces sunshine hours and the amount of radiation received.
The extent of reduction in radiation during this extreme event (January-March 1999) was
relatively lower, as the extent of the diffused radiation increases. During this time, the
reduction ranged from 5-12%. The differential response of the crops (wheat, rice and
sugarcane) under changed proportion of direct and diffused radiation due to haze was
seen through using crop simulation models (WTGROWS for wheat, DSSAT for rice and
sugarcane). The growing conditions were optimal. Regions chosen for simulation were
north-west India for wheat, coastal and southern regions for rice and north-eastern,
western and southern regions for sugarcane. Simulation results were obtained in terms of
phenology, biomass and economic yield at harvest. There was slight reduction in the yield
of these three crops due to reduction in the radiation, but coupled weather changes
(lowering of temperature, etc.) due to cloudy condition could benefit the crops through
phenology modifications and other crop process activities, which can some times give
higher yields of crops under the aerosol layer when compared to no haze layer situation.
Diffused radiation is more photo-synthetically active, and this feature has still to be
included in most of the existing crop growth models, as the existing crop models do not
differentiate between direct and diffused radiation. The scope of using remote sensing for
assessing the haze layer (spatial and temporal extent) could be employed in the crop
simulation models for regional impact analysis.
Land consolidation and GIS application in Xinjiang, China
Author(s):
Junfang Huang;
Ranghui Wang;
Huizhi Zhang
Show Abstract
The growing concern about land resource management and the associated decline in land quality have led to the attention
of land consolidation in many countries. Land consolidation is a tool for improving the effectiveness of land cultivation
and may improve land productivity and possibly also the total factor productivity if it induces and enhances technical
progress and increases scale economies. Land consolidation can also improve labor productivity to supporting rural
development. Consolidation deals with a large number of phenomena, such as fields, roads, and land use, all of which
exhibit characteristic forms and patterns which can be analyzed as to their existing spatial organization, or as to their
changing spatial organization through time. This Paper put forward some approaches and advices about carrying out the
principles of the agro-land consolidation, guiding the ideology, developing the strategy and tidying up the farmland.
Firstly, the main conception and methodology of land consolidation are described. Then, the strengths and weaknesses of
land consolidation in their process are discussed. Finally, as an example, through analyzing of the present condition and
potential of land use and landscape in Beitun Oasis, China, a discussion and conclusions on land consolidation aided by
GIS are presented. This will certainly play an exemplary role in the similar areas of north-west arid zone of China.
Prioritization of Hire Nadi watershed for soil and water conservation measures
Author(s):
Dilip G. Durbude
Show Abstract
For the optimal production, proper management and conservation of the available resources is essential. The assessment of various losses and further prioritization of watershed for the conservation of soil and water resources would be a better task for this purpose. While planning the soil and water conservation measures in the watershed, it is not feasible to take whole area at once. This requires dividing the watershed in small units, which are sub-watersheds. As the condition of each sub-watershed may not be similar, they can be prioritized for conservation work. The present study is carried out in the Hire Nadi watershed, which falls under the drought prone zone of Karnataka state. The agricultural activities of the entire area are mostly depending upon the rainfall and hence the conservation of soil and water resources is the major priority. For the priority fixing, the entire watershed area has been delineated into six sub-watersheds having geographical area from 159 ha to 3199 ha. Thornthwaite-Mather (TM) water balance model and the Universal Soil Loss Equation (USLE) in conjunction with digital image processing through GIS software namely Integrated Land and Water Information System (ILWIS) have been utilized for estimating the various soil and water losses from the watershed. Using the weighted index overlay model for ranking of the sub-watersheds, the entire area is prioritized. It is found that the sub-watershed no. 1 is having first ranking and need immediate attention for soil and water conservation activities to prevent further land degradation and its productivity.
Paddy ground truth data collection and evaluation for land cover mapping
Author(s):
Hiroshi P. Sato;
Ryutaro Tateishi;
Jieying Xiao
Show Abstract
Ground truth data have grid cells that consist of single land cover classification (pure grid cell), and they are useful as the
training data in classifying global land cover. Previous studies showed many global land cover map, however, they have
rarely reported locations and collection method of the training data that they might use. Paddy is indispensable
classification for global land cover mapping but there is no established method for collecting paddy ground truth data. In
this study we collected paddy ground truth candidate data from the existing 1km-resolution China national land use data,
which was produced using 30m-resolution LANDSAT/TM data. Since the land use data recorded classification area
ratio in each grid cell, it is efficient to collect paddy's pure grid cells. We collected pure grid cells of paddy and
segmented them into small areas, and obtained paddy ground truth candidate sites. After the sites whose area >= nine
grid cells (=9 km2) were selected, we examined the selected sites' Normalized Difference Vegetation Index (NDVI)
time-series changes in 2003 using TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) 16-days
composite data. Furthermore, we chose the sites whose NDVI standard deviation =< 0.1 through 2003, as a result, the
number of the selected sites was 271. The 271 candidate sites were assigned to nine China climatic zones, and the sites
that are located near climatic boundaries were eliminated. The site whose area is largest in each climatic zone was tried
to be selected. Finally, ten sites of paddy ground truth data were collected.
Assessment of area under menace of torrents in Shivalik region of Himalayas
Author(s):
A. K. Tiwari;
R. K. Aggarwal;
P. K. Sharma
Show Abstract
Torrents are causing vast area submergence and damage to life, property and
infrastructure almost every year. This is the most common problem in foothills of
Shivaliks, spread over the northern states of India. With the help of modern tools like GIS
and remote sensing techniques, a study has been made to assess the extent of problem
more accurately.Data from Indian Remote Sensing Satellites has been used for gathering
the information about the area under study. The data and imageries were acquired for
different northern states under Shivalik region viz. Jammu and Kashmir, Punjab,
Haryana, Himachal Pradesh, Uttranchal/Uttar Pradesh and Chandigarh (UT). Data
were collected and analyzed with the help of Punjab Remote Sensing Centre Ludhiana
and Regional Remote Sensing Service Centre Dehradun. The results shows that the area
under torrents and rivers was found to be 323 sq. km in Punjab, 400 sq.km in Himachal
Pradesh, 238 sq.km in Haryana, 414 sq.km in Uttar Pradesh/Uttranchal and about 140
sq.km in J and K., which may vary every year depending on the monsoon and other factors
in the Shivalik region. The assessment of the magnitude of the problem provides the
insight and need of reclamation of the affected land and suitable technology to control
the flashy torrents, which do much damage in the rainy season and causes flood in the
downstream areas. About 1500-sq. km area lies under torrents and the area affected can
be up to 7,500 sq. km in the Shivaliks. This demands a
development of economically
viable and suitable technology to combat the menace of torrent in the region.
Changing landscapes: monitoring ecologically sensitive ecosystems in a dynamic semi-arid landscape using satellite imagery: a case study in Ejin Oasis, western China
Author(s):
Meng-Lung Lin;
Yu Cao;
Yi-Huang Tao;
Jyh-yi Shih;
Guo-Jing Yan;
Yung-Tan Lee;
Du-Ning Xiao;
Shin Wang;
Hsien-Hao Chiu
Show Abstract
Land degradation has become an important issue in western China recently. Oasis ecosystem is sensitive to
environmental disturbances, such as abnormal /extreme events of precipitations, water supply from upper watersheds,
fluctuations of temperatures, etc. Satellite remote sensing of terrestrial ecosystems provides temporal dynamics and
spatial distributions of landscape green covers over large areas. Seasonal green cover data are normally important in
assessing landscape health (ex. desertification, rate of urban sprawl, natural disturbances) in arid and semi-arid regions.
In this study, green cover data is derived from vegetation indices retrieved from MODIS sensors onboard Terra. The
satellite images during the period April 2000 to December 2005 are analyzed to quantify the spatial distribution and
temporal changes of Ejin Oasis. The results will help improving monitoring techniques to evaluate land degradation and
to estimate the newest tendency of landscape green cover dynamics in the Ejin Oasis.
Assessment and monitoring of desertification using satellite imagery of MODIS in East Asia
Author(s):
Meng-Lung Lin;
Chieh-Ming Chu;
Jyh-Yi Shih;
Qiu-Bing Wang;
Cheng-Wu Chen;
Shin Wang;
Yi-Huang Tao;
Yung-Tan Lee
Show Abstract
The desertification in Northwestern China and Mongolia shows the result of conflicts between economic
development and natural conservation. Many researches have proven the desert areas are growing in these regions. The
variations of bi-weekly NDVI satellite images are used as one of the parameters to evaluate the vegetation dynamics
over large scale studies. In this study, remotely sensed satellite images are conducted to provide multi-temporal
vegetated and non-vegetated areas in order to assess the status of desertification in East Asia. Spatial data derived from
these satellite images are applied to evaluate vegetation dynamics at regional scale to find out the hot spot areas
vulnerable to desertification. The results show that the desert areas are mainly distributed over southern Mongolia,
central and western Inner-Mongolia, western China (the Taklimakan desert). The desert areas were expanded from 2000
to 2002, were shrunk in 2003, and were expanded from 2003 to 2005 again. The hot spot areas of desertification are
mainly distributed over southeastern Mongolia and eastern Inner-Mongolia. The results will help administrators to refine
the planning processes in defining the boundaries of protected areas and will facilitate to take decision of the priority
areas for conservation of desertification.