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 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 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 EVALUATION OF THE POTENTIAL OF GREEN ROOFS APPLIED TO AN URBAN FABRIC USING GIS AND REMOTE SENSING DATA CASE OF THE NADOR CITY / MOROCCO

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 177-184
Author(s):  
R. Lambarki ◽  
E. Achbab ◽  
M. Maanan ◽  
H. Rhinane
Keyword(s):  
Surface Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Cover ◽  
Green Roofs ◽  
Living Environment ◽  
Green Spaces ◽  
Surface Model ◽  
Landsat 8 ◽  
Gis And Remote Sensing

Abstract. Accelerated urban growth has affected many of the planet's natural processes. In cities, most of the surface is covered with asphalt and cement, which has changed the water and air cycles. To restore the balance of urban ecosystems, cities must find the means to create green spaces in an increasingly gray world. Green spaces provide the city and its inhabitants a better living environment. This article uses Nador city as a case study area, this project consists in studying the possibility for the roofs to receive vegetation. The first axis of this project is the quantification of the current vegetation cover at ground level by calculating the Normalized Difference Vegetation Index (NDVI) based on Satellite images Landsat 8, then the classification of the LiDAR point cloud, and the generation of a digital surface model (DSM) of the urban area. This type of derived data was used as the basis for the various stages of estimating the potential plant cover at the roof level. In order to study the different possible scenarios, a set of criteria was applied, such as the minimum roof area, the inclination and the duration of the sunshine on the roof, which is calculated using the linear model of angstrom Prescott based on solar radiation. The study shows that in the most conservative scenario, 21771 suitable buildings that had to be redeveloped into green roofs, with an appropriate surface area of 369.26Ha allowing a 63,40% increase in the city's green space by compared to the current state contributing to the improvement of the quality of life and urban comfort. The average budget for the installation of green roofs in a building with a surface area of 100 m2 varies between 60000dh and 170000dh depending on the type of green roofs used, extensive or intensive. These results would enable planners and researchers in green architecture sciences to carry out more detailed planning analyzes.

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 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>

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 Utilização de Índices Físicos a partir de Imagens OLI – TIRS para o Mapeamento de Uso e Cobertura da Terra no entorno do Aeroporto Internacional do Recife/Guararapes – Gilberto Freire

2019 ◽  
Vol 12 (3) ◽  
pp. 1039
Author(s):  
Claudianne Brainer De Souza Oliveira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Land Cover Map ◽  
Area Use ◽  
Water Vegetation

Atualmente o uso de índices físicos NDVI (Normalized Difference Vegetacion Index), NDBI (Normalized Difference Built-up Index) e NDWI (Normalized Difference Water Index) vêm sendo muito utilizados como suporte para o mapeamento e monitoramento de uso e ocupação da terra. A área de estudo abrange o Aeroporto Internacional do Recife/Guararapes – Gilberto Freyre e o seu entorno, uma região na qual estão inseridos os municípios de Jaboatão dos Guararapes e Recife, ambos no Estado de Pernambuco. Utilizando imagens do satélite LANDSAT-8, sensor OLI de 18-06-2016, orbita-ponto 214-066, aplicou-se a técnica de fusão RGB-IHS para se obter uma melhor resolução espacial, logo após foram calculados os índices físicos, com o objetivo de avaliar o uso e ocupação do solo da área em questão. Como resultado final, obteve-se um mapa de uso e cobertura da terra, contendo quatro classes (solo exposto, água, vegetação e área construída), na escala de 1:50.000, no sistema de referência geodésico WGS84.  Physical indexes from OLI - TIRS images as tools for land use and coverage mapping around the airport International Recife / Guararapes - Gilberto Freire A B S T R A C TCurrently the use of NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index) and NDWI (Normalized Difference Water Index) have been widely used as support for mapping and monitoring land use and occupation. The study area covers the Recife / Guararapes - Gilberto Freyre International Airport and its surroundings, a region in which the municipalities of Jaboatão dos Guararapes and Recife are located, both in the State of Pernambuco. Using images from the LANDSAT-8 satellite, OLI sensor of 06-06-2016, orbit-point 214-066, the RGB-IHS fusion technique was applied to obtain a better spatial resolution, after the physical indexes were calculated, with the objective of evaluating the land use and occupation of the area in question. As a final result, a land use and land cover map was obtained, containing four classes (exposed soil, water, vegetation and built area), in the 1: 50.000 scale, in the WGS84 geodetic reference system.Keywords: physical indexes, remote sensing, urban area, use and land cover.

scholarly journals UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification

2020 ◽  
Vol 12 (3) ◽  
pp. 529 ◽  
Author(s):  
Hualiang Liu ◽  
Feizhou Zhang ◽  
Lifu Zhang ◽  
Yukun Lin ◽  
Siheng Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
System Modelling ◽  
Temporal Profiles

Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.

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