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

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.

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Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

Remote Sensing ◽

10.3390/rs12162660 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2660

Author(s):

Philip Marzahn ◽

Swen Meyer

Keyword(s):

Remote Sensing ◽

Soil Texture ◽

Land Surface ◽

Temporal Frequency ◽

Management Strategies ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Test Site ◽

Soil Parameters ◽

Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

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

ITB journal of Sciences ◽

10.5614/itbj.sci.2012.44.1.3 ◽

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

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

Remote Sensing ◽

10.3390/rs70404391 ◽

2015 ◽

Vol 7 (4) ◽

pp. 4391-4423 ◽

Cited By ~ 60

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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Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Sustainability ◽

10.3390/su13041824 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1824

Author(s):

Mohamed K. Abdel-Fattah ◽

Elsayed Said Mohamed ◽

Enas M. Wagdi ◽

Sahar A. Shahin ◽

Ali A. Aldosari ◽

...

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Soil Quality ◽

Quality Index ◽

Principal Component ◽

Component Analysis ◽

Soil Samples ◽

Western Desert ◽

Geographical Information ◽

Soil Quality Index

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

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PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-883-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 883-887

Author(s):

Hidetake Hirayama ◽

Mizuki Tomita ◽

Keitarou Hara

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Remote Sensing Data ◽

Future Climate Change ◽

Slope Aspect ◽

Climate Change Scenarios ◽

Vegetation Distribution ◽

Climate Conditions ◽

Distribution Maps ◽

Sensing Data

The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC)，warmth index (WI)， winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.

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PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-883-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 883-887

Author(s):

Hidetake Hirayama ◽

Mizuki Tomita ◽

Keitarou Hara

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Remote Sensing Data ◽

Future Climate Change ◽

Slope Aspect ◽

Climate Change Scenarios ◽

Vegetation Distribution ◽

Climate Conditions ◽

Distribution Maps ◽

Sensing Data

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ENVIRONMENT SENSITIVITY MAPS OF LAND DEGRADATION AND DESRTIFICATION USING MEDULAS MODEL AND REMOTE SENSING IN SHIRQAT CITY/IRAQ

IRAQI JOURNAL OF AGRICULTURAL SCIENCES ◽

10.36103/ijas.v52i3.1361 ◽

2021 ◽

Vol 52 (3) ◽

pp. 697-711

Author(s):

Khalaf & Hussien

Keyword(s):

Remote Sensing ◽

Land Degradation ◽

Quality Index ◽

Clay Soil ◽

Poor Quality ◽

Landsat 8 ◽

Spectral Indices ◽

Soil Quality Index ◽

Moderate Quality ◽

Result Show

This research of aims to study environment sensitivity of desertification and land degradation using MEDULAS project and remote sensing in AL-Shirqat City/Salahadin/Iraq. A 10 soil pedons were chosen from study area depending on difference in soil preperties, landuse and causes of desertification and degradation as (Salinity, Erosion, Gypsum and vegetation cover). Soil profile description, soil samples and GPS were conducted. The physical (texture) and chemical (CaCO3, CaSO4.2H2O, O.M, EC and pH) properties were determined. The Soil were classified as Torrifluvents in the (P1, P2, P3), Torripsamments in the (P5 and P7), Calcigypsids in the (P6, P8 and P10) and Calcids in the P4. The landsat 8 image at 20sep. 2019 and 19 sep. 2013 were aquired in the spectral indices calculate and spatial maps by using ERDAS 15 and GIS 10.2. The result show contrast in soil propreties as sand, clay, soil gypsum, CaCO3, OM and EC that reflect on Soil Quality Index (SQI) which were (60)% poor quality and (40)% moderate quality degradation. While (19.10) % that moderate quality and 80.90% that poor quality for Vegetation Quality Index. The results show that 0.1% of the study area is classiﬁed as C1; 25.35% as C2; 74.55% of the areas as C3. The spectral indices as LAI, SI5, OSAVI were approporiate for monitor of desertification and degradation in study area. Add, spatial change in the spectral indices as NDVI and LAI. The results shown that MEDALUS model is a important model in the areas disposed to desertification and degradation.

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