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1

Use of remote sensing as an indicator of the urban heat island effect: the case of the municipality of Guelma (north-east of Algeria)

Khallef Boubaker

Geomatics, Landmanagement and Landscape

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2023

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no. 3

61--72

EN

The main objective of this study is to show which of the LST-NDVI and LST-NDBI relationships can determine the most accurate index that can be used as an indicator of the effects of urban heat islands in the municipality of Guelma, using Landsat data. 8 OLI/TIRS and the geographic information system. The application of the calculation formulas made it possible to extract the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI) of the municipality of Guelma for the four seasons of 2019. This calculation led to the determination of the relationship between all three indicators. The results obtained show a strong correlation between the LST and the NDBI for the four seasons of the year. They suggest that the NDBI is an accurate indicator of the heat island effect in Guelma. This indicator can serve as a tool for future urban planning by those in charge of this department. However, there is currently and urgent need to strengthen strategies for reducing the effects of urban heat islands in order to preserve the quality of urban life of the inhabitants and by setting up emergency programs.

2

The Use of Spatial Normalized Difference Vegetation Index for Determination of Humus Content in the Soils of Southern Ukraine

Lykhovyd Pavlo

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 4

223--228

EN

Spatial normalized difference vegetation index finds various applications in crop monitoring and prediction. Although this index is mainly aimed to represent the state of vegetation cover, it is suggested that it could be utilized for other remote monitoring purposes, for example, soil humus content monitoring. The study was carried out in 2022–2023 fallow-field period in Kherson oblast, the South of Ukraine, to establish the relationship between the values of bare-soil normalized difference vegetation index and content of humus in the soils of the region. Statistical modeling was performed using the best subsets regression analysis in BioStat v.7 and artificial neural network with back propagation of error algorithm in Tiberius XL. The best performance was recorded for the combined model of cubic regression and artificial neural network, with moderate fitting quality (coefficient of determination is 0.29), and good prediction accuracy (mean average percentage error is 13.22%). The results approve the suggestion of possibility of spatial vegetation index use in soil state monitoring, especially, if further scientific work enhances the fitting quality of the model.

3

Remote Sensing and Meteorological Indexes of Drought Using Open Short Time-Series Data in Doukkala Region, Morocco

Ait Ayad Nada, Ait Ayad Alaaeddine, El Khalidi Khalid, Habib Adnane, Charif Abdelmounim

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 2

1--10

EN

Over time, drought affects all regions of Morocco, especially in the arid climate region, which has negative consequences on agriculture, economic and environmental. The present study aims to describe the intensity of drought in Morocco and more specifically their impact on the distribution of vegetation. Spatial and temporal remote sensing data are used to monitor drought in the Doukkala region of Morocco, using a set of Landsat images, including Landsat 5 (ETM), and Landsat 7 (ETM+) captured during the period 1964–2014. This was determined based on remote sensing parameters: temperature condition index (TCI), vegetation condition index (VCI) and vegetation health index (VHI). The Normalized Difference Vegetation Index (NDVI) was determined for the years 1966, 1984, 1988, 2000 2006 and 2009, in order to identify the vegetation categories and quantify the vegetation density in the Doukkala region. The NDVI obtained was analyzed using the SPI (Normalized Precipitation Index) based on the rainfall data of the years 1966, 1984, 1988, 2000 2006 and 2009. The results obtained showed that the correlation between NDVI and SPI indicated negative values or less than 1. The calculation of VHI showed low values (VHI < 40%) in one part of the studied area that indicate severe to extreme drought conditions, while in the other part the VHI showed high values (VHI > 40%), which mainly reflect favorable conditions for crop development (no drought). The results of this study can be used for monitoring and evaluation of the drought for sustainable management of the area.

4

Monitoring Land Use and Land Cover Changes Using Remote Sensing Techniques and the Precipitation-Vegetation Indexes in Morocco

Ait El Haj Fatiha, Ouadif Latifa, Akhssas Ahmed

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 1

272--286

EN

The study of land use and land cover change (LULC) is essential for the development of strategies, monitoring and control of the ecosystem. The present study aims to describe the dynamics of land cover and land use, and specially the impact of certain climatic parameters on the distribution of vegetation and land cover. For this study, multi-temporal remote sensing data are used to monitor land cover changes in Morocco, using a set of Landsat images, including Landsat 7 (ETM+), Landsat 5 (TM), and Landsat 8 (OLI), captured during the period 2000–2020, those changes were determined by adopting the maximum likelihood (ML) classification method. The classification results show good accuracy values in the range of 90% (2000), 80% (2007), 82% (2010), 93% (2020). The LU/LC change detection showed a decrease of agricultural and forest areas in the order of 5% between the year 2000 and 2020, and an increase of bare soil of 5% to 6%, and a notable change in urban area from 97.31 ha (0.03%) in 2000 to 2988.2637 ha (0.82%) in 2020. The overall results obtained from LULC show that the vegetation cover of the study area has undergone major changes during the study period. In order to monitor the vegetation status, an analysis of the precipitation-vegetation interaction is essential. The normalized difference vegetation index (NDVI) was determined from 2000 to 2020, to identify vegetation categories and quantify the vegetation density in the Lakhdar sub-basin. The obtained NDVI was analyzed using climatic index SPI (Normalized Precipitation Index) based on rainfall data from five stations. The correlation study between NDVI and SPI indices shows a strong linear relation between these two indicators especially while using an annual index SPI12 however, the use of NDVI index based on remote sensing provides a significant result while assessing vegetation. The results of our study can be used for vegetation monitoring and sustainable management of the area, since it is one of the largest basins in the country.

5

Delineating groundwater potential zones using an integrated geospatial and geophysical approach in Phuentsholing, Bhutan

Gyeltshen Sangay, Kannaujiya Suresh, Chhetri Indra Kumar, Chauhan Prakash

Acta Geophysica

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2023

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Vol. 71, no. 1

341--357

EN

Groundwater can serve as an alternative measure to solve the scarcity in perennial water sources. In this perspective, a study has been carried out in Phuentsholing, Bhutan, for demarcating the most probable zone for groundwater source by an integrated application of geospatial and geophysical survey. The seven contributing factors (i.e. geology, geomorphology, drainage, landuse landcover (LULC), normalized difference vegetation index (NDVI), lineament, and slope are evaluated. Subsequently, an Analytic Hierarchy Process (AHP) is also carried out to normalize the weightage and rank of the individual factors, which are further overlaid using the Weighted Index Overlay (WIO) algorithm. The resultant groundwater potential was categorized into: extremely high (0.7%), high (54%), moderate (12.5%), low (21%), and extremely low (12%) potential zones. Each of this category is further validated by Vertical Electrical Sounding (VES-3) using Schlumberger electrode configuration and identified the most probable groundwater exploration zones towards the south-western parts of the study area. Thus, the study emphasizes on significant role of remote sensing and geographic information system (GIS) in aggregation with the geophysical and statistical measures to delineate the most probable location for groundwater resources in the Himalayan region.

6

Exogenous regulation of the potatoes’ adaptive potential when using bio stimulants

Shitikova Aleksandra V., Abiala Adewale A., Tevchenkov Alexander A., Bazhenova Svetlana S., Lazarev Nikolay N., Kurenkova Evgeniya M.

Journal of Water and Land Development

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2022

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no. 54

234--238

EN

Potato from the Solanaceae family is one of the most important crops in the world and its cultivation is common in many places. The average yield of this crop is 20 Mg·ha-1 and it is compatible with climatic conditions in many parts of the world. The experiment studied the possibility of exogenous regulation of the adaptive potential available for four potato cultivars through the use of growth stimulants with different action mechanisms: 24-epibrassinolide (EBL) and chitosan biopolymer (CHT). The results allowed us to establish significant differences in growth parameters, plant height, leaf index, vegetation index, chlorophyll content, and yield structure. Monitoring growth and predicting yields well before harvest are essential to effectively managing potato productivity. Studies have confirmed the empirical relationship between the normalised difference vegetation index (NDVI) and N-tester vegetation index data at various stages of potato growth with yield data. Statistical linear regression models were used to develop an empirical relationship between the NDVI and N-tester data and yield at different stages of crop growth. The equations have a maximum determination coefficient (R2) of 0.63 for the N-tester and 0.74 for the NDVI during the flowering phase (BBCH1 65). NDVI and N-tester vegetation index positively correlated with yield data at all growth stages.

7

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

Nallapareddy Anusha

Applied Computer Science

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2022

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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.

8

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

Gandhi Fenil R., Patel Jayantilal N.

Acta Geophysica

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2022

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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.

9

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

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

Archives of Environmental Protection

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2013

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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.