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

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.

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 MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 370 ◽  
Author(s):  
Luís Flávio Pereira ◽  
Cecilia Fátima Carlos Ferreira ◽  
Ricardo Morato Fiúza Guimarães
Keyword(s):  
Remote Sensing ◽  
Atlantic Forest ◽  
Quality Index ◽  
Minas Gerais ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Mata Atlantica ◽  
Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

scholarly journals Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

2022 ◽  
Vol 14 (2) ◽  
pp. 597
Author(s):  
Paula Godinho Ribeiro ◽  
Gabriel Caixeta Martins ◽  
Markus Gastauer ◽  
Ediu Carlos da Silva Junior ◽  
Diogo Corrêa Santos ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Soil Quality ◽  
Carbon Stock ◽  
Quality Index ◽  
Native Forest ◽  
Spectral Indices ◽  
Soil Quality Index ◽  
Mining Operations ◽  
Soil Carbon Stock ◽  
Soc Stock

Rehabilitation is the key factor for improving soil quality and soil carbon stock after mining operations. Monitoring is necessary to evaluate the progress of rehabilitation and its success, but the use of repeated field surveys is costly and time-consuming at a large scale. This study aimed to monitor the environmental/soil rehabilitation process of an Amazonian sandstone mine by applying spectral indices for predicting soil organic carbon (SOC) stock and comparing them to soil quality index. The studied area has different chronological rehabilitation stages: initial, intermediate, and advanced with 2, 10, and 12 years of onset rehabilitation activities, respectively. Non-rehabilitated (NR) and two native forest areas (RA) were used as controls. Soil samples were analyzed for physical, chemical, and biological attributes. After determination of Normalized Difference Vegetation Index and Bare Soil Index, simple regression analysis comparing these indices with SOC stock showed a good fit (R2 = 0.82). Rehabilitated areas presented higher soil quality index (~1.50-fold) and SOC stock (~10.6-fold) than NR; however, they did not differ of RA. The use of spectral indices was effective for monitoring the soil quality in this study, with a positive correlation between the predicted SOC stock and the calculated soil quality index.

scholarly journals Desertification Detected in the Udhaim River Basin, Iraq Based on Spectral Indices Derived from Remote Sensing Images

2017 ◽  
Vol 21 (3) ◽  
pp. 124-131
Author(s):  
Abd Al Salam Mohammed Mail
Keyword(s):  
River Basin ◽  
Land Degradation ◽  
Water Body ◽  
Human Activities ◽  
Landsat 8 ◽  
Soil Crust ◽  
Spectral Indices ◽  
Climate Conditions ◽  
Tm Image ◽  
Bare Land

AbstractIn this study, changes in Land Use Land Cover (LULC) have been investigated over the Udhaim River Basin in Iraq by using spectral indices. NDVI, NDBI, NDWI, NDBaI, and CI represent respectively the vegetation, built-up, water bodies, bare-land, and soil crust of LULC. Two different images were acquired for the analysis, namely a Landsat 5 TM image from 1 July 2007 and a Landsat 8 OLI from 5 June 2015, both representing summer conditions. Results show that the percentages of vegetated land and water body areas have decreased. On the contrary, the percentages of built-up, bare land and soil crust areas have increased. The loss of vegetated areas and water body areas is a signal of land degradation leading to desertification, due to the combined effects of climate conditions, water deficit and human activities. Field observation shows that human activities have a significant impact on land degradation.

scholarly journals TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

2016 ◽  
Vol III-8 ◽  
pp. 137-144 ◽  
Author(s):  
Gofamodimo Mashame ◽  
Felicia Akinyemi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Degradation ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Land ◽  
Level I ◽  
The Impact ◽  
Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

scholarly journals Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 457 ◽  
Author(s):  
Jose Sobrino ◽  
Rafael Llorens ◽  
Cristina Fernández ◽  
José Fernández-Alonso ◽  
José Vega
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Soil Burn Severity ◽  
Sentinel 2

Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

scholarly journals TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

2016 ◽  
Vol III-8 ◽  
pp. 137-144 ◽  
Author(s):  
Gofamodimo Mashame ◽  
Felicia Akinyemi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Degradation ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Land ◽  
Level I ◽  
The Impact ◽  
Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

scholarly journals An Innovative Approach for the Assessment and Monitoring of Land Degradation and Desertification in Semi-Arid Regions Using Remote Sensing and GIS Techniques

2020 ◽  
Author(s):  
Pradeep Kumar B ◽  
Raghu Babu K ◽  
Rajasekhar M ◽  
Sakram G ◽  
Ramachandra M
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Landsat Imagery ◽  
Satellite Image ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Landsat 8 ◽  
Degraded Land ◽  
Study Region ◽  
Land Degradation And Desertification

Abstract Land degradation (LD) and desertification is a serious ecological, environmental, and social-economic threat in the world, and there is a demanding need to develop accountable and reproducible techniques to assess it at different scales. In this study to assess LD and desertification with the help of Remote Sensing (RS) and Geographical Information System (GIS) in the study region for the period of past 29 years i.e., from 1990 to 2019. The severity of LD and desertification was assessed quantitatively by collecting twelve soil samples in the study region, and analyzing the eleven soil Physico-chemical parameters and these values have made correlated with Digital Number (DN) values with LANDSAT 8 satellite image. The land cover analysis of LANDSAT imagery revealed that the water body slightly increased from 0.29% in 1990 to 0.46% in 2019, and built-up-land increased from 2.87% in 1990 to 5.31% in 2019. Vegetation is decreased from 52.03% in 1990 to 28.57%. Fallow land, degraded land, and desertified lands are increased at alarming rates, respectively 13.71% to 26.35, 18.57% to 22.31%, and 12.53% to 17.00%. It is also established that the multi-temporal analysis of change detection data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 29 years, considerable progress has been made in the respective research.

scholarly journals SOIL QUALITY INDEX ANALYSIS IN EFFORTS TO OVERCOME LAND DEGRADATION IN NGANJUK REGENCY

2020 ◽  
Vol 15 (2) ◽  
pp. 96-106
Author(s):  
Dyah Nursita
Keyword(s):  
Soil Quality ◽  
Land Degradation ◽  
Quality Index ◽  
Quality Criteria ◽  
Biological Indicators ◽  
Minimum Data Set ◽  
Rooting Depth ◽  
Soil Quality Index ◽  
Data Set ◽  
Minimum Data

Abstract Soil quality is ability of soil to preserve the productivity of pants, preserve maintain water supplies, and support human activities. Soil quality assessment results can be used as a recommendation in addressing land degradation. The soil quality cannot be directly measured therefore physical, chemical and biological indicators collectively are determined which influence the soil quality called minimum data set (MDS). A study and experimental analysis was conducted in August - November, 2019. The descriptive study was done in some land units in Nganjuk Regency by measuring its soil index quality using Mausbach and Seybold (1998) criteria which has been modified by Partoyo (2005). The soil quality index was analyzed using function that represented most of the soil. The soil samples were taken by purposive sampling and the texture, volume weight, porosity, C-organic, pH, P-available, K-exchangeable, N-total and rooting depth were analyzed in laboratories. Soil quality index values ranged between 0-1. The higher index value indicates better quality. The analysis result of selected soil functions (MDS) and MDS scores were than summed to determine the value of the soil quality index (SQI). The study concludes that several land units in Ngluyu, Wilangan, and Tanjunganom Districts that had low soil quality (IKT = 0.2399 - 0.3869). Meanwhile, the land units in Bagor District have very good soil quality criteria (IKT = 0.8671). Keywords: soil quality index, land degradation, minimum data set, nganjuk.

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

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