Development of a GIS-based decision support system for assessing land use status

The objectives of this study are to assess land suitability and to predict the spatial and temporal changes in land use types (LUTs) by using GIS-based land use management decision support system. A GIS database with data on climate, topography, soil characteristic, irrigation condition, fertilizer application, and special socioeconomic activities has been developed and used for the evaluation of land productivity for different crops by integrating with a crop growth model—the erosion productivity impact calculator (EPIC). International food policy simulation model (IFPSIM) is also embedded into GIS for the predictions of how crop demands and crop market prices will change under alternative policy scenarios. An inference engine (IE) including land use choice model is developed to illustrate land use choice behavior based on logit models, which allows to analyze how diversified factors ranging from climate changes, crop price changes to land management changes can effect the distribution of agricultural land use. A test for integrated simulation is taken in each 0.1o by 0.1o grid cell to predict the change of agricultural land use types at global level. Global land use changes are simulated from 1992 to 2050.     

Development of a GIS-based decision support system for assessing land use status

IntroductionAgriculturallandusepatternsandtheirchangesaretightlyrelatedwithagriculturepolicyandfoodsecurityissuesundergrowingfooddemand,assess mentofglobalclimatechangeimpactsonagricul ture,environmentalissuesduetotheintensificationofagriculturallandusessuchaswaterpollution,soildegradation,andrecentlywaterscarcityissues.Soasustainableandholisticplanningandmanage mentoflandresourcesshouldcombineallthesere latedinformationwithefficienttoolsforassessmentandevaluationinordertopermitbroad ,interact…     

Geostatistical and visualization analysis of crop suitability for diversification in sub-mountain area of Punjab,North-West India

This study presents a Geographic Information System (GIS)-based geostatistical and visualization analysis of crop suitability in two blocks of sub-mountain area of Punjab under diversification programme. It combines the limitation approach of land capability classification, productivity potential evaluation procedure and crop suitability evaluation framework of FAO. Two blocks from the sub mountain Siwalik region of Punjab viz., Mahalpur and Garhshankar were selected. This study evaluates the capabilities of the study area for traditional crops like wheat, paddy and maize, and recently introduced crops like sugarcane, sunflower, pea, rapeseed-mustard, potatoes and kinnow for agricultural diversification. The suitability of the crops has been worked out at the village level. About 35–40 per cent of total area mostly in Siwallik hills is not fit for growing any type of crop. Sandy texture, uneven topography, moderately steep slopes and excessive drainage are responsible for unsuitability of this area. The GIS based suitability analysis for traditional crops as well as for new crops, under diversification of agriculture has been undertaken. The geostatistical analysis points towards suitability of relatively large areas for new crops like sunflower, potato, pea (green) and sugarcane. Forty three and 14 per cent of total area has been found highly suitable and suitable respectively for growing green pea - a cash crop. Thirty three per cent of total area is suitable for growing kinnow fruit. The success of diversification programme is subject to logical government policy in terms of providing cold storage, food processing facility and marketing infrastructure.     

Geospatial Approach in Assessing Agro-Climatic Suitability of Soybean in Rainfed Agro-Ecosystem

The present study demonstrated the methodology to assess agro-climatic suitability of the soybean crop through integration of crop suitability based on FAO framework of land evaluation and biophysical (water limited) yield potential in the rainfed agro-ecosystem. A long term climatic database (1980–2003) was prepared to compute decadal rainfall and temperature variations of 13 IMD stations in part of Madhya Pradesh state. The climatic database was used in soil water balance software–BUDGET to compute crop specific length of growing period (LGP) and biophysical production potential such as water limited crop yield potential of each soil types for soybean crop. Water limited crop yield potential of soils were found to be varied from 33 to 100 and LGP ranged from 65 to 180 days in the area. FAO based land suitability was analyzed in association with the water limited yield potential for better appraisal of land potential and assess their suitability in rainfed area. FAO based land suitability indicated 2.45 % area as highly suitable and 57.49 % area as moderately suitable. However, integration of water limited crop yield potential with FAO based land suitability lead to agro-climatic suitability analysis indicated 17.60 % and 40.03 % area, respectively as highly suitable and moderately suitable. FAO based land evaluation showed 88.13 % of plains as moderately suitable whereas agro-climatic suitability indicated only 47.79 %. Agro-climatic suitability analysis revealed undulating plateau and undulating plains as most suitable for soybean crop.     

Land suitability assessment for selected crops in Vellore district based on agro-ecological characterisation

Planning based on agro-ecological zoning aims at scientific management of regional resources to meet the food, fibre, fodder and fuel wood requirements without adversely affecting the status of natural resources and environment. An attempt has been made to map the agro-ecological units for Vellore district of Tamil Nadu and derive the crop-zone map for the four major crops namely, paddy, sugarcane, groundnut and millets. The basic theory of FAO framework for Land Evaluation was adopted to define the suitability of crops. Land quality details necessary for evaluating the agro-land suitability of crops and for delineating the agroecological units include the terrain, soil and climatic characteristics. Agro-ecological units map was generated by overlaying the agro-edaphic and agroclimatic map layers in GIS. The agro-land suitability map was generated by matching the crop requirement details with the land qualities. The results of the suitability evaluation, when compared with the current land use statistics of these crops showed that area cultivated is less than the area suitable for these crops.     

Impact Assessment of GIS Based Land Resource Inventory Towards Optimizing Agricultural Land Use Plan in Dandakaranya and Easternghats Physiographic Confluence of India

GIS based land resource inventory (LRI) with fine resolution imagery is considered as most authentic tool for soil resource mapping. Soil resource mapping using the concept of soil series in a smaller scale limits its wide application and also its impact assessment for crop suitability is controversial. In this study, we attempted to develop LRI at large scale (1:10,000 scale) at block level land use planning (LUP) in Dandakaranya and Easternghats physiographic confluence of India. The concept of land management unit was introduced in this endeavour. The impact assessment of LRI based LUP was exercised to develop efficient crop planning with best possible management practices. The study area comprised six landforms with slope gradient ranging from very gentle (1–3%) to steep slopes (15–25%). The very gently sloping young alluvial plains occupied maximum areas (19.95% of TGA). The single cropped (paddy) land appears to dominate the land use systems (40.0% of TGA). Thirty three landscape ecological units were resulted by GIS-overlay. Eighteen soils mapping units were generated. The area was broadly under two soil orders (Inceptisols and Alfisols); three great group (Haplaquepts, Rhodustalfs and Endoaquepts) and ten soil series. Crop suitability based impact assessment of LRI based LUP revealed that average yield of different crops increased by 39.2 and 14.5% in Kharif (rainy season) and Rabi (winter) seasons respectively and annual net returns by 83.4% for the cropping system, compared to traditional practices. Productivity and net returns can be increased several folds if customized recommended practices are adopted by the farmers. Informations generated from the study emphasized the potentiality of LRI towards optimizing LUP and exhibited an ample scope to use the methodology as a tool to assess in other physiographic regions in India and abroad.     

Land suitability studies for major crops in Pavagada taluk,Karnataka using remote sensing and GIS techniques

Pavagada taluk of Tumkur district in Karnataka is one of the most backward taluks receiving less than 500 mm annual rainfall. The maximum area of the taluk is under monocropping, reasons for the same were not documented well. The present study was carried out using remote sensing data along with field survey and laboratory analysis for assessing the potentials and limitations of soil. Using the basic information on soil, climate and topography based on the matching exercise between the growth and production requirements of the crop, suitability of soils for groundnut, paddy and finger millet was assessed as per FAO land evaluation. The soil suitability maps were prepared using Arc GIS software. About 48 per cent of the total area was moderate to marginally suitable and 13 per cent of the area was not suitable for both groundnut and finger millet. Lowland areas covering 12 per cent of the area was highly suitable, 15 per cent was moderate to marginally suitable and 20 per cent was not suitable for paddy cultivation.     

Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Urban land-use suitability analysis — A case study of Calcutta Metropolitan Area

Urban land-use zoning analysis has been carried out to evaluate the suitability of land for urbanisation i.e. a process of assessing the fitness of different land characteristics (soils, physiography, slope, land use etc.) for urban development in Calcutta Metropolitan Development Authority (CMDA) area (1350 sq. km). Remote sensing data in conjunction with Survey of India maps and ancillary data have been employed for the analysis. A GIS-based methodology has been developed for the integration of different physical parameters and generation of urban land development units. In all 82 Urban Land Development Units (ULDUs) were delineated and evaluated with respect to physical constraints and the degree to which each class could limit the development for urban purpose. These ULDUs are categorised into five suitability classes for urban use. The relationships between ULDU classes, suitabilities and urban land development considerations in a generalised form have also been assessed. It has been observed that the physical constraints have become progressively greater from class-I to class-V meaning thereby that the cost for the developing the area for urban purpose increases from class-I and becomes uneconomic for class-V. The suitability map prepared using this methodology would also serve as a sub-set for the preparation of a Master plan of the city.     

Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

The purpose of this study is to present comparative performance analysis of different machine learning algorithms for large area crop classification. Ten Indian districts with significant rabi crops viz. wheat, mustard, gram, red lentils (masoor) have been selected for the study. Most popular classical ensemble models – bagging/ARCing, random forest (RF), gradient boosting and Importance Sampled Learning Ensemble (ISLE) with traditional single model (decision tree) have been selected for comparative analysis. To incorporate dependency of large area crop in different variables viz. parent material and soil, phenology, texture, topography, soil moisture, vegetation, climate etc., 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, Sentinental-1A) and climatic data (precipitation, temperature). In rabi season, field survey about crop type is carried out to prepare training data. Performance is evaluated on the basis of marginal rates, F-measure and Jaccard’s coefficient of community, Classification Success Index and Agreement Coefficients. Score is calculated to rank the algorithm. RF is best performer followed by gradient boosting for crop classification. Other ensemble methods ARCing, bagging and ISLE are in decreasing order of performance. Traditional non-ensemble method decision tree scored higher than ISLE.     

Linking land cover dynamics with driving forces in mountain landscape of the Northwestern Iberian Peninsula

The mountainous areas of the northwestern Iberian Peninsula have undergone intense land abandonment. In this work, we wanted to determine if the abandonment of the rural areas was the main driver of landscape dynamics in Gerês–Xurés Transboundary Biosphere Reserve (NW Iberian Peninsula), or if other factors, such as wildfires and the land management were also directly affecting these spatio-temporal dynamics. For this purpose, we used earth observation data acquired from Landsat TM and ETM + satellite sensors, complemented by ancillary data and prior field knowledge, to evaluate the land use/land cover changes in our study region over a 10-year period (2000–2010). The images were radiometrically calibrated using a digital elevation model to avoid cast- and self-shadows and different illumination effects caused by the intense topographic variations in the study area. We applied a maximum likelihood classifier, as well as other five approaches that provided insights into the comparison of thematic maps. To describe the land cover changes we addressed the analysis from a multilevel approach in three areas with different regimes of environmental protection. The possible impact of wildfires was assessed from statistical and spatially explicit fire data. Our findings suggest that land abandonment and forestry activities are the main factors causing the changes in landscape patterns. Specifically, we found a strong decrease of the ‘meadows and crops’ and ‘sparse vegetation areas’ in favor of woodlands and scrublands. In addition, the huge impact of wildfires on the Portuguese side have generated new ‘rocky areas’, while on the Spanish side its impact does not seem to have been a decisive factor on the landscape dynamics in recent years. We conclude rural exodus of the last century, differences in land management and fire suppression policies between the two countries and the different protection schemes could partly explain the different patterns of changes recorded in these covers.     

Soil erosion assessment and mapping of the algar river watershed (uttar pradesh) using remote sensing technique

This study was undertaken to prepare an inventory on soil erosion of a hilly river watershed — the Aglar watershed, part of Tehri Garhwal and Dehradun districts (U.P.), using terrain physiography and soil survey data obtained from interpretation and analysis of Landsat TM FCC (1:62,500 scale) and limited ground investigations. The watershed is divided into four broad physiographic units viz. higher Himalayas (> 2000m elevation); lower Himalayas (< 2000m elevation); river terraces and flood plains. Each physiographic unit has been further divided into subunits on the basis of aspects and landuse. Three major orders of soils viz. Inceptisols, Mollisols and Entisols were found in different physiographic units. Soil, and land properties of soilscape units viz. soil depth, texture, structure, slope, landuse and soil temperature regime were evaluated for soil-erosion hazard. The results indicate that in the whole watershed 19.13%, 45.68%, 26.51% and 7.92% areas have been found to be under none to slight, moderate, severe and very severe soil erosion hazard categories, respectively.     

Use of GIS and remote sensing for crop diversification — a case study for Punjab state

In this study, an attempt has been made to suggest crop diversification based on soil and weather requirements of different crops. State level spatial databases of various agro-physical parameters such as rainfall, soil texture, physiography and problem soil along with the agricultural area derived from remote sensing data were integrated using GIS. A raster based modelling approach was followed to arrive at suitable zones for practicing different cropping systems. The results showed that the south-western Punjab is suitable for low water requiring crops such as desi cotton, pearl millet, gram etc., where as north-eastern Punjab with high rainfall and excess drainage should practice maize based cropping system. Rice can be substituted by maize and other crops in Central Punjab, where water table is going down fast. Using this approach the area of rice based cropping system can be reduced from present 24.7 lakh ha to 19.6 lakh ha, thereby reducing the degradation of valuable land and water resources.     

农作物单产预测的运行化方法

提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求     

Quantifying determinants of cash crop expansion and their relative effects using logistic regression modeling and variance partitioning

Cash crop expansion has been a major land use change in tropical and subtropical regions worldwide. Quantifying the determinants of cash crop expansion should provide deeper spatial insights into the dynamics and ecological consequences of cash crop expansion. This paper investigated the process of cash crop expansion in Hangzhou region (China) from 1985 to 2009 using remotely sensed data. The corresponding determinants (neighborhood, physical, and proximity) and their relative effects during three periods (1985–1994, 1994–2003, and 2003–2009) were quantified by logistic regression modeling and variance partitioning. Results showed that the total area of cash crops increased from 58,874.1 ha in 1985 to 90,375.1 ha in 2009, with a net growth of 53.5%. Cash crops were more likely to grow in loam soils. Steep areas with higher elevation would experience less likelihood of cash crop expansion. A consistently higher probability of cash crop expansion was found on places with abundant farmland and forest cover in the three periods. Besides, distance to river and lake, distance to county center, and distance to provincial road were decisive determinants for farmers’ choice of cash crop plantation. Different categories of determinants and their combinations exerted different influences on cash crop expansion. The joint effects of neighborhood and proximity determinants were the strongest, and the unique effect of physical determinants decreased with time. Our study contributed to understanding of the proximate drivers of cash crop expansion in subtropical regions.     

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

Estimation of damage to agriculture biomass due to Hudhud cyclone and carbon stock assessment in cyclone affected areas using Landsat-8

Developing countries are vulnerable to tropical cyclones due to climatic variability and the frequency and magnitude of some extreme weather and disaster events is likely to increase. Cities and towns situated along the coastal belt of Visakhapatnam district experienced severe damage because of Hudhub cyclone (12 October 2014). The main objective of this research was to identify and quantify the damage to agriculture and vegetation caused by Hudhud cyclone. In this study, landsat-8 satellite data-sets acquired before and after the cyclone have been used; image processing techniques have been carried out to assess the changes of pre- and post-cyclone condition. Economic loss of agriculture crops has been assessed using crop production loss per hectare and total economic loss for agriculture crops in the study area was calculated. Classification results and land use land cover change analysis show that 13.25% of agriculture-Kharif and 31.1% of vegetation was damaged. Crop Biomass was estimated with aid of crop conversion factor for pre- and post-cyclone conditions. Total ‘Above ground biomass’ of the agriculture crop area was estimated at 31.57 t/ha and total loss of biomass was assessed to be 4.2 t/ha. Carbon stock was found to be varying from 0.3 to 8.3 t.C/ha in specific agriculture crops. From the results, it was concluded that Hudhud has done significant damage to the rural and urban areas of Visakhapatnam. The outcome of this study can be used by decision-makers for the release of post disaster relief fund to affected areas.     

Soil,landuse and land evaluation studies of Patna area Bihar

The soil and landuse surveys have been conducted in Patna area, Bihar, using aerial photos of 1:25,000 scale. Three major systems, Ganges Gandak and interfluvial plain, have been identified in the area. These were further sub-divided into levees plains and channels etc. The soils were classified according to Soil axonomy. The major land use of the area is cultivation (62.2%) (Upland, lowland and wet land crops) plantation (1.8%) habitation (16.0%), water bodies (8.8%), barren lands (9.4%) and miscellaneous (1.8%). The soils of the area have been evaluated for different land utilization types-upland crops, lowland crops, and habitation. For paddy 75.:%, upland crops 8.2% and for habitation 57%, area was found suitable.     

Characterization of biophysical land units using remote sensing and gis

In the present study, an attempt has been made to characterize the biophysical land units in Kanholi bara river basin of sub-humid tropical ecosystem of central India using remotely sensed data, field surveys and GIS based multi-criteria overlay analysis. The geo-spatial database on elevation, slope, landforms, soil depth, soil erosion, land use/land cover and hydrogeomorphological parameters has been generated using IRS-ID LISS-III satellite data coupled with soil survey data in GIS. The methodology followed in characterization of biophysical land units in GIS includes assigning scores for different classes of the layers and weighatges for different layers based on their characteristics and degree of influence on desired output. GIS based ‘multi criteria overlay’ analysis reveals seventeen distinct biophysical land units in the river basin. Severe (50.5-59.5) to very severe (59.5) biophysical stress units are found in plateau spurs, isolated mounds, linear ridges, dissected plateau and escarpments. These zones are associated with severe to very severe erosion, steep to very steep, extremely shallow soils, poor to very poor groundwater prospects, wastelands and scrublands. The characterization of biophysical land units helps in analysis of their potentials, problems and stress environment to plan and execute site-specific landscape management practices and maximize the productivity from each biophysical land unit. The present study demonstrates that generation of geo-spatial database based on remotely sensed data and field surveys in GIS and their analysis helps great extent in characterization of biophysical land units and analysis of their stress environment for management.     

Integrated approach of using remote sensing and GIS to study watershed prioritization and productivity

Soil data obtained from soil resource inventory, land and climate were derived from the remote sensing satellite data (Landsat TM, bands 1 to 7) and were integrated in GIS environment to obtain the soil erosion loss using USLE model for the watershed area. The priorities of different sub-watershed areas for soil conservation measures were identified. Land productivity index was also used as a measure for land evaluation. Different soil and land attribute maps were generated in GIS, and R,K,LS,C and P factor maps were derived. By integrating these soil erosion map was generated. The mapping units, found not suitable for agriculture production, were delineated and mapped as non-arable land. The area suitable for agricultural production was carved out for imparting the productivity analysis; the land suitable for raising agricultural crops was delineated into different mapping units as productivity ratings good, fair, moderate and poor. The analysis performed using remote sensing and GIS helped to generate the attribute maps with more accuracy and the ability of integrating these in GIS environment provided the ease to get the required kind of analysis. Conventional methods of land evaluation procedures in terms of either soil erosion or productivity are found not comparable with the out put generated by using remote sensing and GIS as the limitations in generating the attribute maps and their integration. The results obtained in this case study show the use of different kinds of data derived from different sources in land evaluation appraisals.