scholarly journals Geospatial Effect on the Mining Operation in Joda/Barbil Area of Odisha, India

2021 ◽  
pp. 36-55
Author(s):  
Siba Prasad Mishra ◽  
Kamal Kumar Barik ◽  
Smruti Ranjan Panda
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Surface Air Temperature ◽  
Mining Operation ◽  
Landsat 8 ◽  
Mining Areas ◽  
Illegal Mining ◽  
Comparison Of The Results

The study aims to investigate the Geospatial effect on the extraction operation in Joda and Barbil mining areas of Keonjhar district, Odisha, India. Present work involves the topography, soil, climate, and stratigraphy investigation of the area. The acquisition of Landsat 8 TIRS (Thermal Infrared), Landsat 5 TM (Thematic Mapper), and CARTOSAT DEM data of temporal and spatial satellite images from various websites. ARC GIS and ERDAS IMAGINE 9.2 software used to find the land use and land cover images (accuracy average 90%). Normalized Difference Vegetation Index (NDVI), and Surface air Temperature (SAT) of Barbil area for 2003, 2007, 2017 and 2018 have been estimated. Comparison of the results have shown that, there is increase in built up, and mining areas whereas the agricultural land and vegetation cover are down scaled. There is constant average SAT rise of 1-2°C in all the land cover classification between 2007 and 2018. The NDVI values show conversion of sparse from dense vegetation in the area. Poor operational strategies in mines operation, like corruption, illegal mining, lack of accountability, overburden wastes/ trailing disposal, ecologic degradation, waterlogging in mine pits, and human rights violations are the root causes of environmental deterioration of the study area. It is pertinent to implement strictly, the Mines and Minerals (Development and Regulation) Amendment Act, India, 2021, regular GIS application to assess the mines volume of extraction, strict vigilance and fixation of accountability for losses of existing mines values, and afforestation/ reforestation of degraded/lost forests in Barbil area.

scholarly journals Parametric Methods to Multispectral Image Classification using Normalized Difference Vegetation Index

2019 ◽  
Vol 9 (2S) ◽  
pp. 611-615
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Urban Region ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Barren Soil ◽  
Day By Day ◽  
Manual Verification

The key to proper governance of the municipal bodies lies in knowing the geography of the region. The land cover of the region changes with respect to time. Also, there are seasonal variation in the layout of the waterbodies. Manual verification and surveying of these things becomes very difficult for want of resources. Remote Sensing Images play a very important role in mapping the land cover. In this paper, we consider such remotely sensed Multispectral Images, taken from Landsat-8. Parametric Machine learning algorithm like Maximum Likelihood Classifier has been used on those images to classify the land cover. Normalized Difference Vegetation Index (NDVI) has been calculated and integrates with the classification process. Four basic land covers have been identified for the purpose namely Water, Vegetation, Built-up and Barren soil. The area of study is Bangalore urban region where we find that the water bodies are decreasing day by day. An overall efficiency of 82% with a kappa hat 0f 0.67 has been achieved with the method. The user and the producer accuracies have also been tabulated in the Results part. The results show the land cover changes in a temporal manner

scholarly journals NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

2020 ◽  
Author(s):  
Perminder Singh ◽  
Ovais Javeed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Spectral Signature ◽  
Landsat 8 ◽  
Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining  various features based upon their spectral signature  such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli  was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also  used for the mapping of changes in land use  and land cover.  NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature 

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

scholarly journals Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

2018 ◽  
Vol 10 (12) ◽  
pp. 4363 ◽  
Author(s):  
Patricia Arrogante-Funes ◽  
Carlos Novillo ◽  
Raúl Romero-Calcerrada
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Land Cover Changes ◽  
Mountainous Area ◽  
Statistical Association ◽  
Mountain Areas ◽  
Mountain Ranges ◽  
Statistical Trends

Currently, there exists growing evidence that warming is amplified with elevation resulting in rapid changes in temperature, humidity and water in mountainous areas. The latter might result in considerable damage to forest and agricultural land cover, affecting all the ecosystem services and the socio-economic development that these mountain areas provide. The Mediterranean mountains, moreover, which host a high diversity of natural species, are more vulnerable to global change than other European ecosystems. The protected areas of the mountain ranges of peninsular Spain could help preserve natural resources and landscapes, as well as promote scientific research and the sustainable development of local populations. The temporal statistical trends (2001–2016) of the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) interannual dynamics are analyzed to explore whether the NDVI trends are found uniformly within the mountain ranges of mainland Spain (altitude > 1000 m), as well as in the protected or non-protected mountain areas. Second, to determine if there exists a statistical association between finding an NDVI trend and the specific mountain ranges, protected or unprotected areas are studied. Third, a possible association between cover types in pure pixels using CORINE (Co-ordination of Information on the Environment) land cover cartography is studied and land cover changes between 2000 and 2006 and between 2006 and 2012 are calculated for each mountainous area. Higher areas are observed to have more positive NDVI trends than negative in mountain areas located in mainland Spain during the 2001–2016 period. The growing of vegetation, therefore, was greater than its decrease in the study area. Moreover, differences in the size of the area between growth and depletion of vegetation patterns along the different mountains are found. Notably, more negatives than expected are found, and fewer positives are found than anticipated in the mountains, such as the Cordillera Cantábrica (C.Cant.) or Montes de Murcia y Alicante (M.M.A). Quite the reverse happened in Pirineos (Pir.) and Montes de Cádiz y Málaga (M.C.M.), among others. The statistical association between the trends found and the land cover types is also observed. The differences observed can be explained since the mountain ranges in this study are defined by climate, land cover, human usage and, to a small degree, by land cover changes, but further detailed research is needed to get in-depth detailed conclusions. Conversely, it is found that, in protected mountain areas, a lower NDVI pixels trend than expected (>20%) occurs, whereas it is less than anticipated in unprotected mountain areas. This could be caused by management and the land cover type.

scholarly journals The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

2019 ◽  
Vol 11 (15) ◽  
pp. 4035 ◽  
Author(s):  
Kanat Samarkhanov ◽  
Jilili Abuduwaili ◽  
Alim Samat ◽  
Gulnura Issanova
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Aral Sea ◽  
Landsat 8 ◽  
Spatial And Temporal Patterns ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Cover Maps

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.

scholarly journals Multi-criteria modelling of drought: a study of Brandenburg Federal State, Germany

2021 ◽  
Author(s):  
Christopher Ihinegbu ◽  
Taiwo Ogunwumi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Federal State ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Drought Assessment

AbstractDrought is the absence or below-required supply of precipitation, runoff and or moisture for an extended time period. Modelling drought is relevant in assessing drought incidence and pattern. This study aimed to model the spatial variation and incidence of the 2018 drought in Brandenburg using GIS and remote sensing. To achieve this, we employed a Multi-Criteria Approach (MCA) by using three parameters including Precipitation, Land Surface Temperature and Normalized Difference Vegetation Index (NDVI). We acquired the precipitation data from Deutsche Wetterdienst, Land Surface Temperature and NDVI from Landsat 8 imageries on the USGS Earth Explorer. The datasets were analyzed using ArcGIS 10.7. The information from these three datasets was used as parameters in assessing drought prevalence using the MCA. The MCA was used in developing the drought model, ‘PLAN’, which was used to classify the study area into three levels/zones of drought prevalence: moderate, high and extreme drought. We went further to quantify the agricultural areas affected by drought in the study area by integrating the land use map. Results revealed that 92% of the study area was severely and highly affected by drought especially in districts of Oberhavel, Uckermark, Potsdam-Staedte, and Teltow-Flaeming. Finding also revealed that 77.54% of the total agricultural land falls within the high drought zones. We advocated for the application of drought models (such as ‘PLAN’), that incorporates flexibility (tailoring to study needs) and multi-criteria (robustness) in drought assessment. We also suggested that adaptive drought management should be championed using drought prevalence mapping.

scholarly journals Quantitative Assessment of Land Cover Sensitivity to Desertification in Maigatari Local Government Area, Jigawa State, Nigeria

2020 ◽  
Vol 24 (9) ◽  
pp. 1509-1517
Author(s):  
A. Ahmed ◽  
S. Abba ◽  
F. Siriki ◽  
B. Maman
Keyword(s):  
Land Cover ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Depth ◽  
Plant Cover ◽  
Organic Matter Content ◽  
Matter Content ◽  
Landsat 8 ◽  
Topographic Map

Desertification alludes to land degradation in arid, semi-arid and sub-humid regions resulting from various variables, counting climatic variations  and human activities. When land degradation transpire within the world’s drylands. It regularly makes desert-like conditions. Land degradation  occurs all over, but is characterized as desertification when it occurs within the drylands. The study employed adjusted MEDALUS methodology  using eleven indicators rainfall, evapotranspiration, aridity, soil texture, soil depth, slope gradient, drainage density, plant cover, erosion protection, sensitivity desertification index and Normalized Difference Vegetation Index (NDVI). Remote Sensing and GIS were the main techniques used in the indices computations and mapping. Thus, Shuttle Rader Topographic Map (SRTM) and Landsat 8 satellite imagery for the year 2019 with 30 meter  resolution, captured in the month of August (rainy season), covering the study area were acquired from Global Land cover Facility (GLCF) University of Maryland. The study finds that the duration and intensity of rainfall is declining especially at the edge of the desert, extreme north and western part of the area. Rain quickly drained through infiltration and surface runoff which carried the little nutrients attached to the soil. Rainfall and  climate is of arid type recording about 300-400mm of rainfall and the soil is low in organic matter content making it weak and less fertile and support only the cultivation of cereals and legumes. The study recommends that there is need to strengthen the laws and policies in controlling  desertification and land degradation, establishment of shelterbelts to control desertification and act also as wind breakers and encourage the use of  modern techniques such as drip irrigation to check the rate of infiltration and runoff. Keyword: Desertification; Sensitivity; MEDALUS; GIS; Maigatari

scholarly journals Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

2018 ◽  
Vol 11 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Sunita Singh ◽  
Praveen Kumar Rai
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Observation Data ◽  
Agriculture Land

Abstract Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

scholarly journals Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

2021 ◽  
Author(s):  
Fitsum Temesgen ◽  
Bikila Warkineh ◽  
Alemayehu Hailemicael
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
National Park ◽  
Vegetation Index ◽  
Local Community ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Cultivated Land

AbstractKafta-sheraro national park (KSNP) is one of the homes of the African elephant has experienced extensive destruction of woodland following regular land use & land cover change in the past three decades, however, up to date, data and documentation detailing for these changes are not addressed. This study aims to evaluate the land use land cover change and drivers of change that occurred between 1988 and 2018. Landsat 5(TM), Landsat7 (ETM+), and Landsat 8 (OLI/TIRs) imagery sensors, field observation, and socio-economic survey data were used. The temporal and spatial Normalized difference vegetation index (NDVI) was calculated and tested the correlation between NDVI and precipitation/temperature. The study computed a kappa coefficient of the dry season (0.90) and wet season (0.845). Continuous decline of woodland (29.38%) and riparian vegetation (47.11%) whereas an increasing trend of shrub-bushland (35.28%), grassland (43.47%), bareland (27.52%), and cultivated land (118.36 km2) were showed over thirty years. More results showed bare land was expanded from wet to drier months, while, cultivated land and grazing land increased from dry to wet months. Based on the NDVI result high-moderate vegetation was decreased by 21.47% while sparse & non-vegetation was expanded by 19.8% & 1.7% (36.5 km2) respectively. Settlement & agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected the park resources. Around KSNP, the local community livelihood depends on farming, expansion of agricultural land is the main driver for woodland dynamics/depletion and this leads to increase resources competition and challenges for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity via encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

scholarly journals A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 389-394 ◽  
Author(s):  
R. Bala ◽  
R. Prasad ◽  
V. P. Yadav ◽  
J. Sharma
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sensor Data ◽  
Landsat 8 ◽  
Temperature Relation

<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>

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