Interfacing remote sensing and GIS methods for sustainable rural development

Kushwaha, S. P. S.; Subramanian, S.; Chennaiah, G. Ch.; Murthy, J. Ramana; Rao, Sulakshana; Perumal, Andrew; Behera, G.

doi:10.1080/01431169608949127

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Remote sensing (RS) and Geographic Information System (GIS) techniques help for the planers and decision makers to organize the information and can effectively facilitate in vast amount of spatial information (Kushwaha et al, 1996 andYossif et al, 2016). Geographic Information Systems (GIS) techniques have been used in several studies to identify spatially and evaluate the physical land capability and suitability (Abdel-Motaleb, 1997 andSaleh andBelal, 2014).…”

Section: Introductionmentioning

confidence: 99%

Classification and Mapping of Land Productivity, Capability and Suitability for Production Crops in West El-Minia Governorate, Egypt

Rashed

2020

Journal of Soil Sciences and Agricultural Engineering

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The aim of this work was to classify productivity, capability and suitability indices of soils in west El-Minia Governorate, Egypt. El-Minya Governorate is considered one of the important areas of horizontal expansion in the Upper Egypt. The studied area is covers around 667020 ha and it lies between latitudes of 28°00` and 28°58`N and longitudes of 29°75` and 30°64`E. Nineteen soil profiles were dug to represent the geomorphological units in the studied area. Seven geomorphological units were developed;1-Old river terraces 2-Sand sheets 3-Limestone plateau 4-Peni plains 5-Outwash plain 6-Sand dune 7-Hilly area. In the study area there are two soil orders i.e. Aridisols and Entisols were classified as Typic Torriorthents, Typic Torripsamments and Typic Haplocalcids. Land productivity index classes of the area varies from excellent (C1) to extremely poor (C5) using Riquier etal. (1970). Based on MicroLEIS-CERVATANA model, land capability is grouped into two classes, (S2: good) covers an area of 14.68 % of the total area and the limiting factors are bioclimatic factors and (S3: moderate) covers 81.49% of the total area and the limiting factors are erosion risks, bioclimatic factors, edaphic factors and slope. Twelve crops were selected to evaluate their suitability for agriculture: wheat, maize, potato, sugar beet, cotton, soybean, sunflower, alfalfa, peach, citrus, olive and watermelon. Suitability classes according to MicroLEIS-ALMAGRA model were ranged from soils with suitability (S2) to soils with no suitability (S5) classes depending on soil limitations. The main limitations are soil texture, carbonate content and sodium saturation.

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Section: Introductionmentioning

confidence: 99%

Classification and Mapping of Land Productivity, Capability and Suitability for Production Crops in West El-Minia Governorate, Egypt

Rashed

2020

Journal of Soil Sciences and Agricultural Engineering

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“…The advent of digital classification is that it incorporates spatial adjacency in addition to spectral signature (Argialas and Harlow, 1990;Moller-Jensen, 1990;Wickman and Norton, 1994). Kushwaha et al, (1996) have suggested that land cover itself is spatially contagious. Any classification implies a loss in the internal variability of the cover types since traditional methods of classification (one pixel one category) do not consider the spatial mixtures of land covers (Wamb, 1990;Shimabukuro et al, 1991).…”

Section: Introductionmentioning

confidence: 98%

Forest Vegetation Characterization and Mapping Using IRS-1C Satellite Images in Eastern Himalayan Region

Kushwaha

Roy

2001

Geocarto International

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IRS 1C LISS-III sensor data was used to generate a medium scale vegetation cover map. Four scenes with minimum cloud cover were acquired, pre-processed and geo-referenced to Survey of India (SOI) topomaps. The satellite images were then subjected to knowledge-based hybrid classification. A standard forest vegetation / land cover classification legend was used for this purpose. All the vegetation classes were visited on ground to collect information on their structure and composition, which was utilized in the classification exercise. Total land cover of over 20000 km2 of Subansiri Himalaya was classified into seventeen categories. The vegetation classes derived from digital classification were compared with the existing ground-based forest classification given by Champion and Seth. Area estimates were made for various land cover categories. Distribution of various forest vegetation types when compared with altitudinal zones of the area has shown good relationship. Correspondence using field-gathered GPS points for vegetation classes showed 89.25% overall accuracy. The methodology used here for classification exercise has contributed to improved classification accuracy. All the vegetation classes have been described with respect to their dominant species composition, spectral response on satellite images, occurrence zone with respect to altitude & climate and their correspondence with existing ground-based forest type classification given by Champion and Seth. This study envisages the use of satellite remote sensing and its kindred technologies like GIS and GPS supplemented by ground-based limited field survey for characterizing forest vegetation cover.

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“…The soil survey reports were used to produce both agricultural and urban land use plans. The use of ARC/INFO Geographical information system (GIS) (Arc is supposed to represent the smallest element of a vector data (or line), INFO was the name of the database management tool) tool for digitization of soil survey reports and maps, creating data bases of soil information and linking digital data bases and maps for regional planning in land evaluation programmes (Kushwaha et al, 1996, Maji et al, 2001, Ritung et al, 2007, Bobade et al 2010, Rabia, 2012and Bhaskar et al, 2014. Soil survey and mapping produces a primary data set indicating the range of soils that are encountered, their distribution, and their relative importance.…”

Section: Introductionmentioning

confidence: 99%

Land resource assessment for agricultural development in Seoni district (Madhya Pradesh), India

Bhaskar

Anantwar

Gaikwad

et al. 2016

JANS

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The objective of land resource assessment for rainfed agridevelopment in tribal dominated Seoni ditrict, Madhya Pradesh was to assess the suitability of thrity soil mapping units for sorghum-cotton based systems in relation to fertility constraints and for enhancing crop productivity. The arability and suitability analysis showed that fifty six per cent of arable land is suitable for eleven land use systems. The twenty two per cent of arable basaltic lands in northen plateaus were evaluated as suitable for for citrus, sorghum and soybean cropping systems with limitations of low available nitrogen, phosphorus and zinc whereas in southern precipitous zone , fifteen per cent of granitic lands were evaluated as suitable for sorghum and cotton with limitations of stoniness, low water holding capacity, low status of available nitrogen, phosphorus, potassium and zinc and eighteen per cent of area in sagar and hirvi river valleys for rice, citrus, maize, sunflower and vegetables. Out of 44.6 per cent of nonarable land, thirty seven per cent of land was evaluated as suitable for forestry / grazing and 6.8 per cent for wild life.The study advocates agroecological zonation for maximum utilization of land resources for potential cropping systems in improving produvtivity and fertility management.

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Interfacing remote sensing and GIS methods for sustainable rural development

Cited by 15 publications

References 4 publications

Classification and Mapping of Land Productivity, Capability and Suitability for Production Crops in West El-Minia Governorate, Egypt

Classification and Mapping of Land Productivity, Capability and Suitability for Production Crops in West El-Minia Governorate, Egypt

Forest Vegetation Characterization and Mapping Using IRS-1C Satellite Images in Eastern Himalayan Region

Land resource assessment for agricultural development in Seoni district (Madhya Pradesh), India

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