Spatio-Temporal Analysis and Identification of Deforestation Hotspots in the Moroccan Western Rif

Boubekraoui, Hamid; MAOUNI, Yazid; Ghallab, Abdelilah; DRAOUI, Mohamed; MAOUNI, Abdelfettah

doi:10.2139/ssrn.4327300

Cited by 2 publications

(5 citation statements)

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“…The forest comprises tree-dominant species, including holm oak (Quercus rotundifolia), barberry cedar (Tetraclinis articulata), and red juniper (Juniperus turbinata), often in forest or pre-forest formations (Taïbi et al 2015). Other plant species are common but often in association with holm oak or alone in particular reliefs (high mountains, very steep slopes, escarpments or valley bottoms) (Bouzekraoui et al 2016;Barakat et al 2018).…”

Section: Study Areamentioning

confidence: 99%

“…The barberry cedar extends over tiny areas, constituting islets experiencing a strong regression under the effect of anthropic action. Its populations are remarkably linked to the warm and temperate variants of the semi-arid thermo-Mediterranean and locally lower sub-humid (Benabid 2000) and have a high plasticity and ecological resistance that allow them to colonize all types of geological substrates.…”

Section: )mentioning

confidence: 99%

“…The red juniper grows in the forest in altitudes between 1000 m and 2200 m and strongly occupies a large part of the two northern and southern slopes of the study area. This softwood is characterized by high plasticity and resistance, allowing it to colonize different substrate types (Benabid 2000).…”

Section: )mentioning

confidence: 99%

“…However, this natural heritage is threatened with disappearance due to climatic hazards (desertification) and human intervention (uncontrolled exploitation of wood, evolution of agriculture, overgrazing and fires) (Barakat et al 2018;Bouzekraoui et al 2016;Ettaqy et al 2020). Population growth in a mountainous region with limited agricultural land has reduced forest density.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning based forest species susceptibility mapping using Sentinel 2A data and GIS: A case study of Ait Bouzid forest (Central High Atlas, Morocco)

Barakat,

Brhaiberh,

Ettaqy

et al. 2023

BOGPGS

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In arid and semi-arid environments, producing accurate maps of forest tree cover using optical remote sensing data is essential to understand their spatial distributions and dynamics. In this respect, the current study aimed to explore the effectiveness of support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF)machine learning (ML) models to map the forest tree species of Ait Bouzid region (Central High Atlas, Morocco) by using Sentinel-2A data. The results from all models showed that about 19-28%, 21-27%, 16-24%, 15-18%, and 0,3-0,32% of the area was covered by euphorbia, red juniper, cedar, holm oak, bare ground, and water body, respectively. According to the overall accuracy (OA) and kappa coefficient, the SVM classifier showed the highest OA (73%) and kappa (0.66) values, followed by KNN (OA=70%, kappa=0.62) and RF (OA=67%, kappa=0.59). Regarding LC classes, water, bare soil, and holm oak could be identified with the producer's accuracy attaining 100%, while red juniper and cedar were the most challenging classes to determine for all ML classifiers, with the producer's accuracy of 40-50% and 40-67%. This study revealed the potential of ML approaches coupled with multispectral Sentinel-2A data for forest species cartography in arid areas with high accuracy. Furthermore, it provides crucial information about forest tree species distribution for developing forest management plans.

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Section: Study Areamentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning based forest species susceptibility mapping using Sentinel 2A data and GIS: A case study of Ait Bouzid forest (Central High Atlas, Morocco)

Barakat,

Brhaiberh,

Ettaqy

et al. 2023

BOGPGS

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“…Emerging hotspot analysis is an approach used for statistical spatiotemporal analysis of phenomena. Recently, the approach has been used to detect spatiotemporal patterns of COVID-19 cases [13], landslides [14], invasive species [15] and forest loss [12,[16][17][18]. A few studies were done in Australia [19] and southeast Asia [5,20], utilizing the emerging hotspot analysis method to understand the spatiotemporal patterns of fires.…”

Section: Introductionmentioning

confidence: 99%

National-scale spatiotemporal patterns of vegetation fire occurrences using MODIS satellite data

Mupfiga,

Mutanga,

Dube

2024

PLoS ONE

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As the risk of climate change increases, robust fire monitoring methods become critical for fire management purposes. National-scale spatiotemporal patterns of the fires and how they relate to vegetation and environmental conditions are not well understood in Zimbabwe. This paper presents a spatially explicit method combining satellite data and spatial statistics in detecting spatiotemporal patterns of fires in Zimbabwe. The Emerging Hot Spot Analysis method was utilized to detect statistically significant spatiotemporal patterns of fire occurrence between the years 2002 and 2021. Statistical analysis was done to determine the association between the spatiotemporal patterns and some environmental variables such as topography, land cover, land use, ecoregions and precipitation. The highest number of fires occurred in September, coinciding with Zimbabwe’s observed fire season. The number of fires significantly varied among seasons, with the hot and dry season (August to October) recording the highest fire counts. Additionally, although June, July and November are not part of the official fire season in Zimbabwe, the fire counts recorded for these months were relatively high. This new information has therefore shown the need for revision of the fire season in Zimbabwe. The northern regions were characterized by persistent, oscillating, diminishing and historical spatiotemporal fire hotspots. Agroecological regions IIa and IIb and the Southern Miombo bushveld ecoregion were the most fire-prone areas. The research findings also revealed new critical information about the spatiotemporal fire patterns in various terrestrial ecoregions, land cover, land use, precipitation and topography and highlighted potential areas for effective fire management strategies.

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Spatio-Temporal Analysis and Identification of Deforestation Hotspots in the Moroccan Western Rif

Cited by 2 publications

References 0 publications

Machine learning based forest species susceptibility mapping using Sentinel 2A data and GIS: A case study of Ait Bouzid forest (Central High Atlas, Morocco)

Machine learning based forest species susceptibility mapping using Sentinel 2A data and GIS: A case study of Ait Bouzid forest (Central High Atlas, Morocco)

National-scale spatiotemporal patterns of vegetation fire occurrences using MODIS satellite data

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