Land suitability assessment for perennial crops using remote sensing and Geographic Information Systems: A case study in northwestern Egypt

2006 ◽  
Vol 52 (3) ◽  
pp. 243-261 ◽  
Author(s):  
A. Shalaby ◽  
Y. O. Ouma ◽  
R. Tateishi
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Geographic Information ◽  
Land Suitability ◽  
Perennial Crops ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Predicting the toxicity of post-mining substrates, a case study based on laboratory tests, substrate chemistry, geographic information systems and remote sensing

2017 ◽  
Vol 100 ◽  
pp. 56-62 ◽  
Author(s):  
Cecilie Tesnerová ◽  
Radka Zadinová ◽  
Miroslav Pikl ◽  
František Zemek ◽  
Štěpánka Kadochová ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Laboratory Tests ◽  
Geographic Information

scholarly journals THE UTILIZATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR ANALYSIS OF LAND SUITABILITY FOR THE GROWING OF CIPLUKAN (PHYSALIS ANGULATA L.)

2019 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Nur Adliani ◽  
Nirmawana Simarmata ◽  
Heriansyah Heriansyah
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Land Suitability ◽  
Suitability Analysis ◽  
Land Suitability Analysis ◽  
Sensing Data ◽  
Physalis Angulata

Remote sensing data and geographic information systems are widely used for land suitability analysis for crops such as coffee and corn. This study aims to analyze and map suitable land for the plant known locally as ciplukan (Physalis angulata L.).  As the cultivation of this plant is expected to be developed by the Institute of Technology of Sumatra, analysis of this type is needed. The parameters used in this study were slope, land use, rainfall and soil type. Information extraction from remote sensing data was carried out via visual interpretation of aerial photography used to create land-cover maps. Shuttle RADAR Topographic Mission (SRTM) data was converted from digital surface model (DSM) to digital terrain model (DTM) to provide elevation information. Land suitability analysis was performed using a scoring method and overlay analysis. The results obtained from the analysis identified several classes of land suitability for Physalis angulata L., categorized as suitable, less suitable, and not suitable. The less suitable class, scored at 9 to 11, comprised a total area of 180.96 ha, while the suitable area, scored at 12, comprised a total area of 49.1 ha.

scholarly journals Mapping of land potentially for maize plant in Madura Island-Indonesia using remote sensing data and geographic information systems (GIS)

2020 ◽  
Author(s):  
Suhartono Suhartono ◽  
◽  
Agoes Soegianto ◽  
Achmad Amzeri
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Remote Sensing Data ◽  
Secondary Data ◽  
Geographic Information ◽  
Land Suitability ◽  
Sensing Data ◽  
Maize Productivity ◽  
Maize Cultivation

Maize productivity in Indonesia was still low (5.241 tons/ha) compared to the average of the ten largest maize producing countries in the world (6.179 tons/ha). The potential for maize on the island of Madura is approximately 360,000 hectares. The potential for maize cultivation in Madura continues to decrease in land quality due to improper land clearing and land-use change. The purpose of this research was to make a map of land suitability for maize using Remote Sensing Data and Geographic Information System (GIS). The land suitability method for maize plants used satellite imagery as a data source, supported by fieldwork and secondary data. Data analysis using Geographic Information Systems (GIS). The results of the analysis of land suitability modeling based on agroecosystem potential found that most of the Madura area was suitable for maize cultivation. Madura island had a land area of 456,622.3ha for maize cultivation, where 170.379.5 (15.4%) was very appropriate, 211.412.3 ha (46.3%) was appropriate, 160,098.6 (35.1%) was less appropriate, and 14,732.0 ha (3.2%) was not appropriate.

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