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

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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Assessment and Monitoring of Land Degradation Using Geospatial Technology in Bathinda District, Punjab, India

10.5194/se-2016-172 ◽

2017 ◽

Cited By ~ 1

Author(s):

Naseer Ahmad ◽

Puneeta Pandey

Keyword(s):

Land Degradation ◽

Near Infrared ◽

Soil Samples ◽

Geographical Information ◽

Landsat 8 ◽

Infrared Band ◽

Salt Intrusion ◽

Efficient Manner ◽

Barren Land ◽

Geospatial Technology

Abstract. Land degradation leads to alteration in ecological and economic functions due to 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 by analysing the physico-chemical parameters in the laboratory and correlating them with satellite based studies.The results revealed that the soils in the study area were exposed to the 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 largely found slightly or moderately saline with a few salt-free sites. Further, 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 the year 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 km2 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 15.2602 km2 in the year 2014. Significant correlation was observed between the Digital Number (DN) of 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 a baseline data to combat land degradation and conserve land resources in an efficient manner.

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Protection of Ecosystem and Preservation of Biodiversity: The Geospatial Technology Approach

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2021.01.0253 ◽

2021 ◽

Vol 5 (1) ◽

pp. 67-75

Author(s):

S. O. Ogunlade

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Google Earth ◽

Geographical Information ◽

Reference Image ◽

Special Focus ◽

Landsat 8 ◽

Spatial Transformation ◽

Geospatial Technology ◽

Bare Land

he protection of ecosystem and preservation of biodiversity through the approach of geospatial technology was the aim of this research. The channel was monitoring the spatial transformation of the Federal University of Technology, Akure, Nigeria between year 2002 and year 2018 using Satellite Remote Sensing and Geographical Information System techniques. Landsat 7 Enhanced Thematic Mapper (ETM) plus of year 2002, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) of year 2014 and year 2018 all of 32m resolution were the satellite images obtained for the study. These images were processed with supervised maximum likelihood classification algorithm using ArcGIS 10.3 software. To validate the classification and ensure high accuracy, an accuracy assessment was performed using training samples from 60 points on each of the satellite imagery on a reference image from google earth combined with ground data collected on actual visitation to the study area to verify the true land-cover type existing on the site. The resultant images deemed fit for analyses were classified into built-up, thick vegetation, light vegetation and bare land, land cover classes. Microsoft Excel spreadsheet was used to perform land cover area calculations through which the land cover dynamics and the spatial expansion were identified. The result showed built-up (13.58%, 14.59%, 20.75%); thick vegetation (33.78%, 26.26%, 12.18%); Light vegetation (24.57%, 32.29%, 30.51%); Bare land (28.08%, 26.26%, 36.56%) for the three years respectively. A special focus was put on the general depletion of the (thick and light) vegetation of which trees are a major actor. These depletion were adduced to the positive transformation of other land cover classes through the underlining landuse. The study concluded that alteration, depletion and consequent disappearance of trees in the green ecosystem is a threat to environment’s sustainability and the protection of ecosystem and preservation of biodiversity. The study recommended the research as a tool to controlling the removal of trees and thick forest, growing more trees and plants among other factors to protect ecosystem and preserve biodiversity.

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Monitoring and Analysis of Degraded Land in Rasulabad Block of Kanpur Dehat District Using Remote Sensing and GIS Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37768 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2413-2421

Author(s):

Pushkin Kumar

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Land Degradation ◽

Soil Degradation ◽

Agricultural Practices ◽

Soil Samples ◽

Lower State ◽

Soil Parameters ◽

Soil Maps ◽

Gis Environment

Abstract: Land degradation is seen as a development or additional that reduces current and/or potential soil capability to produce products and goods. This implies a decline from a higher to a lower state due to a decline in land capacity, productivity, and biodiversity loss. This can be both natural and human-induced. Natural causes embody earthquakes, tsunamis, droughts, avalanches, landslides, volcanic eruptions, floods, tornadoes, and wildfires. Whereas human-induced soil degradation results from land clearing and deforestation, inappropriate agricultural practices, improper management of industrial effluents and wastes, over-grazing, careless management of forests, surface mining, urban sprawl, and commercial/industrial development. Inappropriate agricultural practices embody excessive tillage and use of heavy machineries, excessive and unbalanced use of inorganic fertilizers, poor irrigation and water management techniques, chemical or pesticide overuse, inadequate crop residue and organic carbon inputs, and poor crop cycle planning. Some underlying social causes of soil degradation in Asian nation square measure land shortage, decline in per capita land handiness, economic pressure onto land, land occupancy, poverty, and population increase.. The aim of the current study is to prepare baseline data to combat land degradation and conserve land resources in an economical and efficient manner. To assess land degradation with the help of Remote Sensing (RS) and Geographical Information System (GIS) – in Rasulabad Block of Kanpur Dehat district, Uttar Pradesh, different levels of analysis were performed to estimate the extent of land. Degradation to assess saline or salt-free soils and calcareous or sodium soils and to match this data with satellite studies. The spatial variability of these soil parameters was shown in soil maps created in a GIS environment. A temporary study of the 2017 and 2021 Sentinel satellite datasets was done to find the parameters that are responsible for land degradation. The severity of land degradation was calculable quantitatively by analyzing the physico-chemical parameters within the laboratory to see salinity and sodicity of soils and further correlating them with satellite-based studies. The pH varied between 7.1 and 8.2, electrical conductivity (EC) between 0.23 and 0.6 miliSiemens/m and the methyl orange or total alkalinity between 0.095 and 0.225 (HCO3 ) gL-1 as CaCO3. The spatial variability in these soil parameters was pictured through soil maps generated in a GIS environment with the help of IDW Interpolation. The results revealed that the soil in the study area was exposed to salt intrusion, most of the soil samples of the study area were slightly or moderately saline with a few salt-free sites. Moreover, the majority of the soil samples were calcareous and a few samples were alkaline or sodic in nature. Keyword: Land degradation, Sodic land, Saline land, GIS, IDW Interpolation.

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

10.21203/rs.3.rs-56088/v1 ◽

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.

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Decomposition and Nitrogen Dynamics of Tree Pruned Biomass Under Albizia Procera Based Agroforestry System in Semi Arid Region of Bundelkhand, India

Current World Environment ◽

10.12944/cwe.12.3.24 ◽

2017 ◽

Vol 12 (3) ◽

pp. 725-733

Author(s):

Garima Gupta ◽

R S Yadav ◽

Deepak Maurya

Keyword(s):

Water Body ◽

Vegetation Index ◽

Forest Cover ◽

Normalized Difference Vegetation Index ◽

Research Work ◽

Agroforestry System ◽

Geographical Information ◽

Landsat 8 ◽

Barren Land ◽

Effective Manner

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

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Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v5i2.920 ◽

2016 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Muhammad Danish Siddiqui ◽

Arjumand Z Zaidi

Keyword(s):

Satellite Data ◽

Spatial Scales ◽

Economic Value ◽

Vegetation Indices ◽

Cost Effective ◽

Geographical Information ◽

Landsat 8 ◽

Coverage Area ◽

Breeding Sites ◽

Marine Plant

Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation wasachieved with WV-2 data that shows 15.5Ha (0.155 Km2)of seaweed cover along Karachi coast that is more representative of the field observed data. A much larger area wasestimated with Landsat 8 image (71.28Ha or 0.7128 Km2) that was mainly due to the mixing of seaweed pixels with water pixels. The WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat 8 in mapping seaweed patches

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A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽

10.3390/land10030231 ◽

2021 ◽

Vol 10 (3) ◽

pp. 231

Author(s):

Can Trong Nguyen ◽

Amnat Chidthaisong ◽

Phan Kieu Diem ◽

Lian-Zhi Huo

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Urban Areas ◽

Near Infrared ◽

Urban Landscape ◽

Bare Soil ◽

Critical Element ◽

Landsat 8 ◽

Climate Conditions ◽

Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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A Novel Index for Impervious Surface Area Mapping: Development and Validation

Remote Sensing ◽

10.3390/rs10101521 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1521 ◽

Cited By ~ 6

Author(s):

Yugang Tian ◽

Hui Chen ◽

Qingju Song ◽

Kun Zheng

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

Impervious Surface ◽

Urban Heat Islands ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Capital Cities ◽

Heat Islands ◽

Surface Areas ◽

Hydrological Cycles

The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

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Impacts of Urbanization on Land Use Pattern and Environment: A Case Study of Ajmer City, Rajasthan

Asian Review of Social Sciences ◽

10.51983/arss-2019.8.1.1514 ◽

2019 ◽

Vol 8 (1) ◽

pp. 87-91

Author(s):

Bhanu Priya Chouhan ◽

Monika Kannan

Keyword(s):

Land Use ◽

Urban Growth ◽

Land Degradation ◽

Satellite Images ◽

Agricultural Land ◽

Population Pressure ◽

Geographical Information ◽

Land Use Patterns ◽

Use Patterns ◽

The Impact

The world is undergoing the largest wave of urban growth in history. More than half of the world’s population now lives in towns and cities, and by 2030 this number will swell to about 5 billion. ‘Urbanization has the potential to usher in a new era of wellbeing, resource efficiency and economic growth. But due to increased population the pressure of demand also increases in urban areas’ (Drakakis-Smith, David, 1996). The loss of agricultural land to other land uses occasioned by urban growth is an issue of growing concern worldwide, particularly in the developing countries like India. This paper is an attempt to assess the impact of urbanization on land use and land cover patterns in Ajmer city. Recent trends indicate that the rural urban migration and religious significance of the place attracting thousands of tourists every year, have immensely contributed in the increasing population of city and is causing change in land use patterns. This accelerating urban sprawl has led to shrinking of the agricultural land and land holdings. Due to increased rate of urbanization, the agricultural areas have been transformed into residential and industrial areas (Retnaraj D,1994). There are several key factors which cause increase in population here such as Smart City Projects, potential for employment, higher education, more comfortable and quality housing, better health facilities, high living standard etc. Population pressure not only directly increases the demand for food, but also indirectly reduces its supply through building development, environmental degradation and marginalization of food production (Aldington T, 1997). Also, there are several issues which are associated with continuous increase in population i.e. land degradation, pollution, poverty, slums, unaffordable housing etc. Pollution, formulation of slums, transportation congestion, environmental hazards, land degradation and crime are some of the major impacts of urbanization on Ajmer city. This study involves mapping of land use patterns by analyzing data and satellite imagery taken at different time periods. The satellite images of year 2000 and 2017 are used. The change detection techniques are used with the help of Geographical Information System software like ERDAS and ArcGIS. The supervised classification of all the three satellite images is done by ERDAS software to demarcate and analyze land use change.

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