Articles
Remote sensing-based analysis of tree degradation in the Haouz Plain: examining the impact of drought and its spatial extent
Article number
1422_21
Pages
167 – 174
Language
English
Abstract
Persistent trees, such as olive trees, are crucial resources in arid and semi-arid regions.
Over the past years, the decrease in water irrigation resources, due to climate change and underground water overexploitation, has led to notable degradation of tree orchards.
This study aims to realize a spatial analysis of tree degradation across a semi-arid area called the Haouz plain located in central Morocco using remote sensing data.
For that, the supervised classification method, specifically the decision tree method, was first employed to extract average thresholds for tree identification spanning the years 2013 to 2022. After, a drying speed index (DS) was calculated based on the normalized difference vegetation index (NDVI) extracted from Landsat-8 data over identified trees.
Therefore, the DS was correlated with the standardized precipitation index (SPI) to establish the linkage between tree degradation and drought (precipitation deficit). The obtained results revealed a declining trend in trees, indicating a mean decrease in the NDVI of permanent tree cover by 0.05 between 2019 and 2022 compared to the reference period (2013-2019). This decline has affected an area of 37.550 ha, with 42% experiencing degradation exceeding the average.
High correlation coefficients (R) were observed between DS and some SPI. These findings highlighted the effectiveness of NDVI for tree land cover classification and underscored the impact of drought on tree degradation in the Haouz plain.
Over the past years, the decrease in water irrigation resources, due to climate change and underground water overexploitation, has led to notable degradation of tree orchards.
This study aims to realize a spatial analysis of tree degradation across a semi-arid area called the Haouz plain located in central Morocco using remote sensing data.
For that, the supervised classification method, specifically the decision tree method, was first employed to extract average thresholds for tree identification spanning the years 2013 to 2022. After, a drying speed index (DS) was calculated based on the normalized difference vegetation index (NDVI) extracted from Landsat-8 data over identified trees.
Therefore, the DS was correlated with the standardized precipitation index (SPI) to establish the linkage between tree degradation and drought (precipitation deficit). The obtained results revealed a declining trend in trees, indicating a mean decrease in the NDVI of permanent tree cover by 0.05 between 2019 and 2022 compared to the reference period (2013-2019). This decline has affected an area of 37.550 ha, with 42% experiencing degradation exceeding the average.
High correlation coefficients (R) were observed between DS and some SPI. These findings highlighted the effectiveness of NDVI for tree land cover classification and underscored the impact of drought on tree degradation in the Haouz plain.
Publication
Authors
Y. Ablila, A. Amazirh, S. Khabba, E.H. Bouras, M.H. Kharrou, S. Er-Raki, A. Chehbouni
Keywords
drying speed, normalized difference vegetation index, precipitation deficit, natural resources management, semi-arid regions
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