Land suitability assessment for potential surface irrigation of river catchment for irrigation development in Kansai watershed, Purulia, West Bengal, India

2017 ◽  
Vol 4 (4) ◽  
pp. 699-714 ◽  
Author(s):  
Biplab Mandal ◽  
Gour Dolui ◽  
Sujan Satpathy
Keyword(s):  
West Bengal ◽  
Potential Surface ◽  
Land Suitability ◽  
Surface Irrigation ◽  
Irrigation Development ◽  
River Catchment ◽  
Suitability Assessment ◽  
Land Suitability Assessment

scholarly journals Soil Nutrient Based Land Suitability Analysis for Lentil Crop in Tarakeswar, Hooghly, West Bengal

2020 ◽  
Author(s):  
Kishore Chandra Swain ◽  
Chiranjit Singha
Keyword(s):  
West Bengal ◽  
Pairwise Comparison ◽  
Soil Nutrient ◽  
Exponential Model ◽  
Land Suitability ◽  
Suitability Analysis ◽  
Available Nitrogen ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Gis Software

Background: Land suitability assessment can inform decisions on land uses suitable for maximizing crop yield while making best use, but not impairing the ability of natural resources such as soil to support development. We assessed the suitability of lentil to be produce in 300 ha land of Tarakeswar block of Hooghly district West Bengal. Methods: Suitability criteria included eight criterion, such as: soil texture (ST), electrical conductivity (EC), organic carbon (OC), available nitrogen(N), available phosphorous (P),available potassium(K) and available zinc (Zn). We modiûed and used a novel set of techniques to assess suitability: Analytical Hierarchy Process (AHP) pairwise comparison matrixand Geographic Information System (GIS) software. Result: The exponential model generated by Ordinary Kriging was used to estimate the lentil land use suitability. Suitability distribution of deep layer found nearly 42% area under suitability class, i.e. S1-2 which may be used for growing the crop profitably.

Study on the Design and Matching of the Measures for Karst Rocky Desertification Control in Small Catchment Scale

2012 ◽  
Vol 518-523 ◽  
pp. 4557-4563
Author(s):  
Shi Zhen Xiao ◽  
Bin Ying ◽  
Kang Ning Xiong ◽  
Kun Feng Deng ◽  
Liang Tang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Suitability ◽  
Cultivated Land ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Small Catchment ◽  
River Catchment ◽  
Desertification Control ◽  
Suitability Assessment ◽  
Land Suitability Assessment

The Renjiang River Catchment in Zunyi City, Guizhou Province of China was selected as the study area to research how to design and match the measures for rocky desertification control and ecosystem restoration. SPOT satellite image (resolution 2.5m), the topographic maps (1:10000), regionalism Map (1:50000), hydrogeological map were used to research under the 3S-Technology (GPS, RS, GIS) and software including ArcView, ArcGIS and Erdas. Many times of field survey and discussions with the local people and government were also carried out. Conclusions were drawn that: the design and match of the measures for the Renjiang River Catchment is scientific and rational; land use conditions, rocky desertification grades, land suitability assessment and prediction of the cultivated land demanded are very important for the design and matching of the measures for the rocky desertification control; a comprehensive idea for each small catchement how to select and design individual measure and how to match the measures together to achieve the best effect of rocky desertification control is also important. Finally, suggestions are given for the karst rocky desertification areas that land use conditions, rocky desertification grades, land suitability assessment and prediction of the cultivated land demanded should be fully considered in the process of designing and matching the measures; the land use should be matched with the rocky desertification and should support the scale of measured designed; measures can be implemented only when these measures do not threat the basic cultivated land demand; the land suitability should also support the area of the measures designed.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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