A cloud-integrated GIS for forest cover loss and land use change monitoring using statistical methods and geospatial technology over northern Algeria

Bilel Zerouali; Celso Augusto Guimarães Santos; Thiago Victor Medeiros do Nascimento; Richarde Marques da Silva

doi:10.1016/j.jenvman.2023.118029

A cloud-integrated GIS for forest cover loss and land use change monitoring using statistical methods and geospatial technology over northern Algeria

J Environ Manage. 2023 Sep 1:341:118029. doi: 10.1016/j.jenvman.2023.118029. Epub 2023 May 10.

Authors

Bilel Zerouali¹, Celso Augusto Guimarães Santos², Thiago Victor Medeiros do Nascimento³, Richarde Marques da Silva⁴

Affiliations

¹ Vegetal Chemistry-Water-Energy Laboratory, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali, University of Chlef, B.P. 78C, Ouled Fares, 02180, Chlef, Algeria. Electronic address: bilel.zerouali@yahoo.fr.
² Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900, João Pessoa, Paraíba, Brazil.
³ Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900, João Pessoa, Paraíba, Brazil; Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
⁴ Department of Geosciences, Federal University of Paraíba, 58051-900, João Pessoa, Paraíba, Brazil.

PMID: 37172351
DOI: 10.1016/j.jenvman.2023.118029

Abstract

Over the last two decades, forest cover has experienced significant impacts from fires and deforestation worldwide due to direct human activities and climate change. This paper assesses trends in forest cover loss and land use and land cover changes in northern Algeria between 2000 and 2020 using datasets extracted from Google Earth Engine (GEE), such as the Hanssen Global Forest Change and MODIS Land Cover Type products (MCD12Q1). Classification was performed using the pixel-based supervised machine-learning algorithm called Random Forest (RF). Trends were analyzed using methods such as Mann-Kendall and Sen. The study area comprises 17 basins with high rainfall variability. The results indicated that the forest area decreased by 64.96%, from 3718 to 1266 km², during the 2000-2020 period, while the barren area increased by 40%, from 134,777 to 188,748 km². The findings revealed that the Constantinois-Seybousse-Mellegue hydrographic basin was the most affected by deforestation and cover loss, exceeding 50% (with an area of 1018 km²), while the Seybouse River basin experienced the highest percentage of cover loss at 40%. Nonparametric tests showed that seven river basins (41%) had significantly increasing trends of forest cover loss. According to the obtained results, the forest loss situation in Algeria, especially in the northeastern part, is very alarming and requires an exceptional and urgent plan to protect forests and the ecological system against wildfires and climate change. The study provides a diagnosis that should encourage better protection and management of forest cover in Algeria.

Keywords: Algeria; Forest cover; Geographic information system; Google earth engine; Management; Nonparametric methods.

MeSH terms

Agriculture
Algeria
Conservation of Natural Resources* / methods
Environmental Monitoring / methods
Geographic Information Systems*
Humans
Technology