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

2017 ◽  
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.

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

Solid Earth ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 75-90 ◽  
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.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat 8 ◽  
Infrared Band ◽  
Bare Land ◽  
Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam)

2020 ◽  
Vol 956 (2) ◽  
pp. 40-49
Author(s):  
Le Hung Trinh ◽  
Dinh Sinh Mai ◽  
V.R. Zablotskii
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Satellite Images ◽  
Near Infrared ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Infrared Band ◽  
The Status ◽  
The Impact ◽  
Sentinel 2

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

scholarly journals Assessing drought from satellite data to support agricultural production

2020 ◽  
Vol 4 (1) ◽  
pp. First
Author(s):  
Van Tran Thi ◽  
Toi Nguyen Duong Lam ◽  
Huynh Phan Thi Diem ◽  
Ha Nguyen Ngan ◽  
Bao Ha Duong Xuan
Keyword(s):  
Surface Temperature ◽  
Social Life ◽  
Vegetation Index ◽  
Near Infrared ◽  
Global Climate ◽  
Landsat 8 ◽  
Agricultural Crop ◽  
Infrared Band ◽  
Drought Assessment ◽  
Optical Satellite Images

Drought is one of the disasters causing the problems to the economy and social life of people, especially where agriculture is the main source of income. The paper presents the results of studying the application of optical satellite images to investigate the drought situation for the southern part of Binh Phuoc province for perennial cropland, the main agricultural crop of the province. The image used is Landsat 8 of the dry season month 2015. The method of drought assessment is based on the relationship of surface temperature, and the Normalization Difference Vegetation Index (NDVI) integrated into the Temperature-Vegetation Dryness Index TVDI. In particular, the NDVI index is determined from the red and near-infrared bands, and the surface temperature is determined from the thermal infrared band of Landsat 8 images. The results show that the whole area of southern Binh Phuoc has drought area accounting for 54.9% of the total area, of which the majority is mild drought level 38.3%, high and serious level is 16.7%. About the area of perennial land has drought area accounted for 33.76% of the total area, of which Dong Xoai town has the highest percentage of drought-affected areas compare to other districts. The results of the study aimed to identify drought areas with different levels so that managers can promptly take measures to protect agricultural crops and to ensure people's livelihoods in the global climate change trend seriously affecting the localities today.

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

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.

scholarly journals Modelagem de variáveis dendrométricas em povoamentos florestais utilizando imagens de média resolução espacial (Modeling of biophysical variables in forest stands using medium spatial resolution images)

2016 ◽  
Vol 9 (6) ◽  
pp. 1969
Author(s):  
Elisiane Alba ◽  
Emanuel Araújo Silva ◽  
Juliana Marchesan ◽  
Letícia Pedrali ◽  
Rudiney Soares Pereira ◽  
...  
Keyword(s):  
Population Density ◽  
Near Infrared ◽  
Tree Density ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Forest Stands ◽  
Infrared Band ◽  
Radiometric Correction ◽  
Vegetation Indexes

Objetivou-se avaliar as imagens Landsat 8/OLI na obtenção de estimativas do volume florestal e densidade populacional de plantios de E. grandis. Para tanto, utilizaram-se 42 unidades amostrais de povoamentos com 18 e 25 anos, mensurando-se os parâmetros dendrométricos Diâmetro à Altura do Peito (DAP), altura total e densidade de árvores. Foi realizada a correção radiométrica da imagem Landsat 8/OLI, obtendo a reflectância de superfície das bandas e índices de vegetação, a qual foi relacionada com as variáveis florestais, ajustando equações de estimativas por meio do método forward. Para os plantios com 18 anos, a equação ajustada explicou 87% da variabilidade do volume com as variáveis SAVI e NDVI presentes no modelo. A densidade populacional foi explicada pelo SR e DVI (R²=0,56). Aos 25 anos, o modelo contendo a banda do infravermelho próximo (B5) e o índice SR respondeu a 92% da variação total do volume florestal.  Nesta idade, a densidade populacional não apresentou correlação positiva. As propriedades espectrais da imagem apresentaram sensibilidade às variáveis dendrométricas, permitindo o monitoramento do desenvolvimento dos povoamentos florestais, justificando a aplicabilidade deste método.    A B S T R A C T This study aims at evaluate Landsat 8/OLI images in obtaining of estimates of the volume and tree density in plantations E. grandis. Therefore, was used 42 sampling unities of stands with 18 e 25 years, measurand the dendrometric parameters Diameter at Breast Height, total height and tree density. Was performed the radiometric correction of the Landsat 8/OLI image, obtaining the surface reflectance of the bands and vegetation indexes, which was related with variables forestry, adjusting equation of estimates through of the method forward. For plantations with 18 years, adjusting equation explained 87% of the volume variability with the variables SAVI and NDVI present in the model. Already the population density was explained by indexes SR and DVI (R²= 0.56). At 25 years, the model containg the near infrared band (B5) and the SR index responded to 92% of the total variation of the volume forestry. This age, the population density showed no positive correlation. The spectral properties of the image demonstrated sensitivity to variables dendrometric, allowing the monitoring of the development of forest stands, justifying the applicability of this method. Keywords: index vegetation, spectral reflectance, wood volume.   

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.

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