A Simple GIS-Based Tool for the Detection of Landslide-Prone Zones on a Coastal Slope in Scotland

González-Ollauri, Alejandro

doi:10.3390/land10070685

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…Other methods also include object-based image analysis, which differs in the way pixels are grouped or how homogeneous pixels are converted into objects [22,32,33], and the accuracy of the results often depends on the geographical characteristics of the study area [34]. The automated approaches are simple [35,36], and cost-effective without the need for physical interventions [37]. Some of the research studies used the SCP for application in various land cover classifications, e.g., [38].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Tempa,

Ilunga,

Agarwal

et al. 2024

Applied Sciences

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Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

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Section: Introductionmentioning

confidence: 99%

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Tempa,

Ilunga,

Agarwal

et al. 2024

Applied Sciences

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“…The performance of these remote sensing techniques often depends on the geographical characteristics of the study area [26]. The automated approaches are simple [27,28], and cost-effective without the need for physical interventions [29]. Some of the research studies used the SCP for application in various land cover classification, e.g., [24,30].…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

Tempa

Aryal²

2022

SN Appl. Sci.

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The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source, semi-automatic classification plugin tools in QGIS software enabled efficient and rapid conduct of land cover classification. Band sets 2-8, 8A, and 11-12 are utilized and the virtual colour composites have been used for the clustering and creation of training samples or regions of interest. An iterative self-organizing data analysis technique is used for clustering and the image is classified by a minimum distance algorithm in the unsupervised classification. The Random Forest (RF) classifier is used for the supervised classification. The unsupervised classification shows an overall accuracy of 85.47% (Kappa coefficient = 0.71) and the RF classifier resulted in an accuracy of 92.62% (Kappa coefficient = 0.86). A comparison of the classification shows a higher overall accuracy of the RF classifier with an improvement of 7.15%. The study highlights 35.59% (512,100 m2) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards. Article highlights Semi-automatic classification technique was applied to delineate the geohazard-prone area in the heterogeneous region of Bhutan Himalaya. Unsupervised and supervised classification technique were used to perform land cover classification using the semi-automatic classification plugin (SCP). The Random Forest classifier predicted higher accuracy and the application is rapid and efficient compared to the unsupervised classification.

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A case history of co-design and co-deployment of a nature-based solution (NbS) against erosion and slope instability

Mickovski,

Gonzalez-Ollauri,

Sorolla

et al. 2024

Ecological Engineering

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A Simple GIS-Based Tool for the Detection of Landslide-Prone Zones on a Coastal Slope in Scotland

Cited by 7 publications

References 30 publications

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

A case history of co-design and co-deployment of a nature-based solution (NbS) against erosion and slope instability

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