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 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.

A new method of sample selections for optimizing phenology model based remote sensing data

2015 ◽  
Vol 39 (3) ◽  
pp. 264-274
Author(s):  
MA Yong-Gang ◽  
◽  
ZHANG Chi ◽  
CHEN Xi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
New Method ◽  
Sensing Data ◽  
Model Based ◽  
Phenology Model

A PRELIMINARY STUDY ON THE RE_INITIALIZATION/RE_PARAMETERIZATION OF A CROP MODEL BASED ON REMOTE SENSING DATA

2005 ◽  
Vol 29 (6) ◽  
pp. 918-926
Author(s):  
MA Yu-Ping ◽  
◽  
WANG Shi-Li ◽  
ZHANG Li ◽  
HOU Ying-Yu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Model Based ◽  
Preliminary Study

Analysis model based recovery of remote sensing data

2014 ◽  
Author(s):  
Xinghua Li ◽  
Huanfeng Shen ◽  
Huifang Li ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Analysis Model ◽  
Sensing Data ◽  
Model Based

scholarly journals Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

2020 ◽  
Vol 12 (24) ◽  
pp. 4037
Author(s):  
Zhi Li ◽  
Xiaomei Yang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Automated Detection ◽  
Urban Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

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
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