Wyniki wyszukiwania - Biblioteka Nauki

1

Detection and classification of vegetation areas from red and near infrared bands of LANDSAT-8 optical satellite image

100%

Nallapareddy A.

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tom Vol. 18, no 1

45--55

EN

Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive. In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possi-bilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified.

2

Regional Mapping of Land Surface Temperature (LST), Land Surface Emissivity (LSE) and Normalized Difference Vegetation Index (NDVI) of South-South Coastal Settlements of Rivers State in Nigeria

86%

Nwaerema P. , Ajiere S.

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tom 28

76-86

EN

This study is the regional mapping of Land Surface temperature (LST), Land Surface Emissivity (LSE) and Normalized Difference Vegetation Index (NDVI) of south-south coastal settlements of Rivers State in Nigeria. The Google Earth Engine (GEE) of satellite remote sensing origin was used in the study. It was observed that land surface area of the south-south coastal settlements of the region hosting a total population of 3,344,706 persons had undergone severe modification and alteration of vegetal cover by increased human activities especially in the central area. Emissivity in the region increased from the center to the rural settlements with values ranging 0.98 to 0.99 and difference of 0.01 indicating that there was increased modification of the regional land surface. Land surface temperature decreased from the regional center to the rural settlements ranging between 22.12 ºC to 35.99 ºC with a difference of 13.87 ºC. However, LST was scattered in different settlement spots especially in the northern region such as Aleto, Finema (south); Rumuolu, Odogwa, Abara, Umuechem, Rumuola, Ambroda (north) among others. The normalized vegetation index showed -0.54358 to 0.409327 having the difference of 0.952907 indicating greater variation in vegetal cover across the region. Thus, NDVI in the region increased from the regional center to the outskirts of the area. Urbanization in the south-south region of Rivers State had extended severely to the rural settlements. Therefore, it is recommended that policy makers and regional planners should protect the area from adverse vegetal lost and heat effects by implementing regional greening practices.

3

Land cover change assessment of vaal harts irrigation scheme using multi-temporal satellite sata

86%

Otieno F. A. , Ojo O. I. , Ochieng G. M.

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tom Vol. 39, no. 4

59--70

EN

Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

4

Groundwater potentiality deciphering and sensitivity study using remote sensing technique and fuzzy approach

58%

Gandhi F. R. , Patel J. N.

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tom Vol. 70, no 1

265--282

EN

Elevating industrialization and urbanization have increased water demand, resulting in a water crisis and plummeting groundwater resources day by day. The present research proposed a model to decipher groundwater potential zones by integrating remote sensing (RS) data with fuzzy logic in an ArcGIS environment. Eleven groundwater potentiality influencing factors have been employed for the study. Each layer was passed through a multicollinearity check, resulting in no collinearity found between the layers. Furthermore, each layer was reclassified, ranked according to their potential to the groundwater occurrence, and assigned fuzzy values. The groundwater potential zones were developed by applying an overlay operation to integrate eleven fuzzy layers. According to the fuzzy value, the Surat district is divided into four potential zones: very poor, poor, moderate, and good. The result shows that 32.21% (1343 km2 ) and 31.63% (1319 km2 ) have good and moderate groundwater potential zones, respectively. Additionally, the map removal sensitivity study illustrated that drainage density, lineament density, and rainfall are more sensitive to potential zones in the study area. The potential zones have been verified by a false matrix, indicating substantial agreement between groundwater levels and potential zones with an overall accuracy of 81.1%. Thus, the integration of RS data and fuzzy-based method is an efficient method for deciphering groundwater potential zones and can be applied anywhere with necessary adjustment.