Wyniki wyszukiwania - BazTech

1

Integration of Sentinel2 Data into a GIS System for the Mapping of Areas at Risk of Degradation by Applying the MEDALUS Model – Case of Sidi Mohamed Ben Abdellah Watershed (Morocco)

Ouakil Abdelhadi, Eddefli Fatimazahra, Ben Hichou Badr, Chao Jamal, Elbelrhiti Hicham

Ecological Engineering & Environmental Technology

|

2023

|

Vol. 24, iss. 7

216--228

EN

Located in the central northwest of Morocco, the Sidi Mohamed Ben Abdellah dam watershed is particularly exposed to soil degradation risk due to a combination of factors such as wide exposure, lithological heterogeneity, and varying climatic conditions. Therefore, the purpose of the conducted study was to create a spatial map of the areas most susceptible to degradation using the MEDALUS (Method for the Evaluation of the Degree of Soil Loss Susceptibility) model to pinpoint the areas that are most vulnerable to the risk of erosion. The MEDALUS model is a commonly used tool for assessing soil degradation and erosion risks. It takes into account physical, climatic, and land use factors to determine the susceptibility of an area to soil loss. To apply the model to the Sidi Mohamed Ben Abdellah dam watershed, the data on factors such as slope, soil type, vegetation cover, and precipitation would be collected, and this information would be used to generate a map of the areas at greatest risk of erosion. This map could then be used to prioritize conservation and management efforts in the watershed and identify the areas that require additional protection or restoration. The map of erosion sensitivity is produced by combining factors that contribute to the phenomenon, such as vegetation cover, climate, relief, pedology, and human intervention. Cross-referencing these factors in a GIS (geographic information system) allows generating an erosion sensitivity map that highlights the most vulnerable areas to this hazard in the region.

2

Integrated Geomatics and Remote Sensing Analysis of Forest Fire Propagation and Land Cover Change in Berkane, Morocco

Ben Hichou Badr, Mhammdi Nadia, Dakki Mohamed

Ecological Engineering & Environmental Technology

|

2023

|

Vol. 24, iss. 9

347--357

EN

By combining geomatics techniques and remote sensing data, this paper gives a thorough investigation of the forest fires that occurred close to Berkane, Morocco, from July 16 to July 18, 2023. The goals of the study included spatiotemporally tracking the propagation of active forest fires during the fire season, and to accurately map the burned area and detect changes in vegetation cover caused by the fire. A detailed fire severity mapping of the impact of the fire on the forest was made by this integrated approach. We used remote sensing data from various sources, including NASA FIRMS data for the fire period and Sentinel-2 satellite imagery acquired two days before and one day after the fire, to accomplish these goals. In terms of estimating the burned area, our study produced important findings. We were able to estimate 3508.12 hectares, 3517.98 hectares, and 3113.63 hectares using satellite imagery with dNBR, dNDVI, and supervised classification, respectively. These results offer considerable potential for directing post-fire management plans and preserving this critically important forest area. The integration of FIRMS data, Sentinel-2 images, and GIS in our research highlights the need of using this coordinated strategy to conduct an accurate and thorough evaluation of forest fires in the area. In addition to improving our understanding of forest fire dynamics, this study emphasizes the value of using cutting-edge geospatial and remote sensing techniques in attempts to manage wildfires and save the environment. The findings of this study will contribute significantly to guiding post-fire management strategies, thus promoting the conservation of the vital forest area.

3

Assessment of Change in Urban Green Spaces Using Sentinel 2 MSI Data and GIS Techniques: A Case Study in Thanh Hoa City, Vietnam

Nguyen Viet Nghia, Trinh Le Hung, Nguyen Thi Thu Nga, Le Thi Le

Inżynieria Mineralna

|

2021

|

no. 2

251--260

EN

This paper presents the results of an assessment of change in urban green spaces in Thanh Hoa city (Vietnam). Sentinel 2 MSI data in 2015 and 2021 are used to calculate 3 parameters: percentage of green, weight of green types, and weight of proximity to green. These parameters are used to calculate the Weighted Urban Green Space Index (WUGSI). The final result shows the distribution of green space in the study area consisted of very high-quality green, high-quality green, moderate quality green, and low quality green. The obtained results show that the quality of urban green space in Thanh Hoa city has changed significantly in the period 2015-2021, in which the area with category “low quality green space” increased from 7.17% up to 9.48%; areas with category “very high-quality green space” reduced from 65.02% to 47.39%.

4

An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq

Alwan Imzahim A., Aziz Nadia A.

Geomatics and Environmental Engineering

|

2021

|

Vol. 15, no. 1

5--21

EN

Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.