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

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.

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SPATIAL-TEMPORAL CHANGES OF LAND DEGRADATION CAUSED BY NATURAL AND HUMAN INDUCED FACTORS: CASE STUDY OF BULGAN PROVINCE IN CENTRAL MONGOLIA

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

10.5194/isprs-archives-xliii-b4-2021-79-2021 ◽

2021 ◽

Vol XLIII-B4-2021 ◽

pp. 79-85

Author(s):

S. Dalantai ◽

E. Sumiya ◽

Y. Bao ◽

M. Otgonbayar ◽

U. Mandakh ◽

...

Keyword(s):

Land Use ◽

Land Degradation ◽

Geographical Information ◽

Land Use Type ◽

Degraded Land ◽

Gis And Remote Sensing ◽

Central Mongolia ◽

Temperature And Precipitation ◽

Multi Temporal ◽

Land Degradation And Desertification

Abstract. Land degradation and desertification have been ranked as a major environmental issue for arid and semi-arid regions is a comprehensive concept that depends on many factors. Detecting early land degradation is a significant issue of social and environmental with geographical information system (GIS) and remote sensing methods has been used for the interpretation of spatial-temporal data. In this study, the assessment of the current state of land degradation is influenced by several complexes of the natural and anthropological causes. The results of land degradation assessment carried out for Bulgan province of Mongolia using multi-temporal resourced data as climate condition (vegetation growing season of temperature and precipitation), land use type (density of seasonal camps of herder households, roads, cropland, settlements) and MODIS vegetation product data were used to estimate land degradation change period from 2000 to 2018 and accessed it’s for effecting on degradation over last 19 years. We obtained a prediction of land degradation integrated with indicators and based on the spatial pattern of human influence. One of the main indicators for land degradation was land use type as pasture usage of livestock husbandry in Bulgan province, overgrazing is the most widespread cause of land degradation, particularly around permanent location of herders and livestock affecting about moderately and slightly degraded land is 72.78% of study total area.

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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Assessment and monitoring of land degradation using geospatial technology in Bathinda district, Punjab, India

Solid Earth ◽

10.5194/se-9-75-2018 ◽

2018 ◽

Vol 9 (1) ◽

pp. 75-90 ◽

Cited By ~ 8

Author(s):

Naseer Ahmad ◽

Puneeta Pandey

Keyword(s):

Land Degradation ◽

Near Infrared ◽

Soil Samples ◽

Geographical Information ◽

Total Alkalinity ◽

Soil Parameters ◽

Landsat 8 ◽

Sodic Soils ◽

Barren Land ◽

Geospatial Technology

Abstract. Land degradation leads to alteration of ecological and economic functions due to a decrease in productivity and quality of the land. The aim of the present study was to assess land degradation with the help of geospatial technology – remote sensing (RS) and geographical information system (GIS) – in Bathinda district, Punjab. The severity of land degradation was estimated quantitatively by analyzing the physico-chemical parameters in the laboratory to determine saline or salt-free soils and calcareous or sodic soils and further correlating them with satellite-based studies. The pH varied between 7.37 and 8.59, electrical conductivity (EC) between 1.97 and 8.78 dS m−1 and the methyl orange or total alkalinity between 0.070 and 0.223 (HCO3−) g L−1 as CaCO3. The spatial variability in these soil parameters was depicted through soil maps generated in a GIS environment. The results revealed that the soil in the study area was exposed to salt intrusion, which could be mainly attributed to irrigation practices in the state of Punjab. Most of the soil samples of the study area were slightly or moderately saline with a few salt-free sites. Furthermore, the majority of the soil samples were calcareous and a few samples were alkaline or sodic in nature. A comparative analysis of temporal satellite datasets of Landsat 7 ETM+ and Landsat 8 OLI_TIRS of 2000 and 2014, respectively, revealed that the water body showed a slight decreasing trend from 2.46 km2 in 2000 to 1.87 km2 in 2014, while the human settlements and other built-up areas expanded from 586.25 to 891.09 km2 in a span of 14 years. The results also showed a decrease in area under barren land from 68.9847 km2 in 2000 to 15.26 km2 in 2014. A significant correlation was observed between the digital number (DN) of the near-infrared band and pH and EC. Therefore, it is suggested that the present study can be applied to projects with special relevance to soil scientists, environmental scientists and planning agencies that can use the present study as baseline data to combat land degradation and conserve land resources in an efficient manner.

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Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Atmosphere ◽

10.3390/atmos12121599 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1599

Author(s):

Linshan Tan ◽

Kaiyuan Zheng ◽

Qiangqiang Zhao ◽

Yanjuan Wu

Keyword(s):

Remote Sensing ◽

Meteorological Data ◽

Regional Scale ◽

Landsat 8 ◽

Surface Reflectance ◽

Huaihe River ◽

Infiltration Capacity ◽

Mountain Area ◽

Study Region ◽

Sebal Model

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

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Remote Sensing in Predicting Development Patterns of Built-up Land

Jurnal Geosains dan Remote Sensing ◽

10.23960/jgrs.2021.v2i2.61 ◽

2021 ◽

Vol 2 (2) ◽

pp. 56-64

Author(s):

Iqbal Eko Noviandi ◽

Ramadhan Alvien Hanif ◽

Hasanah Rahma Nur ◽

Nandi

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Population Growth Rate ◽

Landsat Imagery ◽

Temporal Analysis ◽

West Java ◽

Development Patterns ◽

Human Needs ◽

Multi Temporal ◽

Total Growth

Indonesia is a developing country whose construction and development are centered on the island of Java, especially in West Java Province. Sukabumi City is one of the areas in West Java. The development of urban areas is expanding due to various human needs to carry out the construction of buildings. Remote sensing that can be used to store developments with multi-temporal analysis with materials is Landsat imagery from 2001 to 2020. The method used is the Normalized Difference Built-up Index (NDBI). The purpose of this study is to map the development of the built-up land from year to year and predict the following years. The results of the research on the significant changes in built-up land occurred between 2013-2020, while from 2001 to 2013 there was not much change. Based on the research results, the total growth of built-up land was 1.539% per year with a population growth rate of 1.4% per year. The results of the analysis show that the area of land built in Sukabumi City in 2028 is 186,7194 km2 or has increased by 21,2808 km2 since 2020.

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A study on deforestation in hilly areas of Haryana using Remote Sensing and GIS technique

Environment Conservation Journal ◽

10.36953/ecj.2020.21308 ◽

2020 ◽

pp. 69-77

Author(s):

Anju Jangra ◽

Anurag Airon ◽

Ram Niwas

Keyword(s):

Remote Sensing ◽

Forest Cover ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Geographical Information ◽

Special Focus ◽

Landsat 8 ◽

Deforestation Rate ◽

Gis Technique ◽

Prime Concern

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.

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Land and Forest Management by Land Use/ Land Cover Analysis and Change Detection Using Remote Sensing and GIS

Journal of Landscape Ecology ◽

10.1515/jlecol-2016-0005 ◽

2016 ◽

Vol 9 (1) ◽

pp. 63-77 ◽

Cited By ~ 4

Author(s):

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Management ◽

Land Cover ◽

Spatial Data ◽

Management Strategies ◽

Geographical Information ◽

Spatial Data Analysis ◽

Study Region ◽

Rugged Terrain

Abstract Remote sensing and Geographical Information System (GIS) are the most effective tools in spatial data analysis. Natural resources like land, forest and water, these techniques have proved a valuable source of information generation as well as in the management and planning purposes. This study aims to suggest possible land and forest management strategies in Chakia tahsil based on land use and land cover analysis and the changing pattern observed during the last ten years. The population of Chakia tahsil is mainly rural in nature. The study has revealed that the northern part of the region, which offers for the settlement and all the agricultural practices constitutes nearly 23.48% and is a dead level plain, whereas the southern part, which constitute nearly 76.6% of the region is characterized by plateau and is covered with forest. The southern plateau rises abruptly from the northern alluvial plain with a number of escarpments. The contour line of 100 m mainly demarcates the boundary between plateau and plain. The plateau zone is deeply dissected and highly rugged terrain. The resultant topography comprises of a number of mesas and isolated hillocks showing elevation differences from 150 m to 385 m above mean sea level. Being rugged terrain in the southern part, nowadays human encroachment are taking place for more land for the cultivation. The changes were well observed in the land use and land cover in the study region. A large part of fallow land and open forest were converted into cultivated land.

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Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽

10.3390/proceedings2231430 ◽

2018 ◽

Vol 2 (23) ◽

pp. 1430

Author(s):

V. M. Fernández-Pacheco ◽

C. A. López-Sánchez ◽

E. Álvarez-Álvarez ◽

M. J. Suárez López ◽

L. García-Expósito ◽

...

Keyword(s):

Remote Sensing ◽

Air Pollutants ◽

Satellite Images ◽

Satellite Image ◽

Remote Sensing Image ◽

Regional Studies ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Landsat 5 Tm ◽

Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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Determination of Drought Threshold for Agricultural Land-use using Satellite Image Analysis Techniques

10.5194/egusphere-egu2020-12421 ◽

2020 ◽

Author(s):

Jieun Kim ◽

Jaehyung Yu ◽

Sang Kee Seo ◽

Jin-Hee Baek ◽

Byung Chil Jeon

Keyword(s):

Remote Sensing ◽

Land Use ◽

Near Infrared ◽

Agricultural Land ◽

Irrigation Management ◽

Satellite Image ◽

Agricultural Drought ◽

Agricultural Land Use ◽

Landsat 8 ◽

Eastern Area

<p>The climate change causes major problems in natural disasters such as storms and droughts and has significant impacts on agricultural activities. Especially, global warming changed crops cultivated causing changes in agricultural land-use, and droughts along with land-use change accompanied serious problems in irrigation management. Moreover, it is very problematic to detect drought impacted areas with field survey and it burdens irrigation management. In South Korea, drought in 2012 occurred in western area while 2015 drought occurred in eastern area. The drought cycle in Korea is irregular but the drought frequency has shown an increasing pattern. Remote sensing approaches has been used as a solution to detect drought areas in agricultural land-use and many approaches has been introduced for drought monitoring. This study introduces remote sensing approaches to detect agricultural drought by calculation of local threshold associated with agricultural land-use. We used Landsat-8 satellite images for drought and non-drought years, and Vegetation Health Index(VHI) was calculated using red, near-infrared, and thermal-infrared bands. The comparative analysis of VHI values for the same agricultural land-use between drought year and non-drought year derived the threshold values for each type of land-use. The results showed very effective detection of drought impacted areas showing distinctive differences in VHI value distributions between drought and non-drought years.</p>

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Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

HAYATI Journal of Biosciences ◽

10.4308/hjb.26.3.117 ◽

2019 ◽

Vol 26 (3) ◽

pp. 117

Author(s):

Tri Muji Susantoro ◽

Ketut Wikantika ◽

Agung Budi Harto ◽

Deni Suwardi

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Landsat 8 ◽

Gas Field ◽

Research Results ◽

The Third ◽

Third Phase ◽

The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

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