Mapping land degradation and desertification using remote sensing data

2006 ◽  
Author(s):  
S. K. Saha ◽  
Munish Kumar ◽  
Bhajan Lal ◽  
Alok Kumar Barman ◽  
Satyendra Nath Das
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Land Degradation And Desertification

scholarly journals Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1426
Author(s):  
Ahmed S. Abuzaid ◽  
Mohamed A. E. AbdelRahman ◽  
Mohamed E. Fadl ◽  
Antonio Scopa
Keyword(s):  
Remote Sensing ◽  
Soil Quality ◽  
Land Degradation ◽  
Quality Index ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Western Desert ◽  
Soil Quality Index ◽  
Climate Conditions ◽  
Sensing Data

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

scholarly journals Land Degradation Model Based on Vegetation and Erosion Aspects Using Remote Sensing Data

2012 ◽  
Vol 44 (1) ◽  
pp. 19-34
Author(s):  
Adhi Wibowo ◽  
Ishak H. Ismullah ◽  
Bobby S. Dipokusumo ◽  
Ketut Wikantika
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Degradation Model ◽  
Sensing Data ◽  
Model Based

scholarly journals Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011)

2015 ◽  
Vol 7 (4) ◽  
pp. 4391-4423 ◽  
Author(s):  
Sergio Vicente-Serrano ◽  
Daniel Cabello ◽  
Miquel Tomás-Burguera ◽  
Natalia Martín-Hernández ◽  
Santiago Beguería ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Drought Indices ◽  
Sensing Data ◽  
Drought Variability ◽  
Semiarid Regions

Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis

2021 ◽  
pp. 146900
Author(s):  
Rita Marcia da Silva Pinto Vieira ◽  
Javier Tomasella ◽  
Alexandre Augusto Barbosa ◽  
Silvia Palotti Polizel ◽  
Jean Pierre Henry Balbaud Ometto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Sensing Data
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