article · Trees Forests and People
An assessment of land use and land cover trends in the Kaffa biosphere reserve evaluated satellite imagery from 1986 to 2019 and projected changes to 2049. The findings revealed significant losses in natural ecosystems over this period. Settlement areas expanded by 157 per cent and agricultural land grew by 87.9 per cent. In contrast, grasslands shrank by 74.3 per cent, forest cover decreased by 30.5 per cent, and wetlands diminished by 18.1 per cent. Key drivers behind these transformations included population growth, resettlement initiatives, semi-forest coffee farm investments, and weak monitoring of participatory forest management users. These pressures have degraded the quality and availability of ecosystem services. Addressing these shifts requires coordinated landscape planning, including clear zonation between conservation territories and economic development. The generated spatial data and maps offer baseline evidence to support future land management and conservation strategies.
Tracking deforestation and land conversion highlights how human settlement and agricultural expansion degrade critical natural ecosystems. Understanding specific local drivers, such as commercial farming investments and resettlement, provides environmental managers and policymakers with the precise evidence required to design balanced zoning plans that protect biodiversity while supporting regional development.
The generated land use maps and spatial data serve as decision-support tools for conservation authorities, regional planners, and environmental consultancies. This represents applied research rather than a standalone commercial product, positioned to directly assist public bodies and development organisations in designing sustainable land use zonation and monitoring natural resource investments.
AI-generated from the published abstract. Always read the original work before citing.
Accurate and up-to-date information on spatial and temporal changes in land use and land cover (LULC) is helpful in understanding the relationship between the environment and society. The main objective of this study was to quantify the overall trends in LULC changes and the rate of forest loss between 1986 and 2019 and projects for the years 2034 and 2049. Landsat satellite imagery acquired in 1986, 1999, 2009 and 2019 were selected to classify the LULC types with a supervised classification algorithm that uses the maximum likelihood classifier technique. The Kappa coefficient (90–92%) and overall accuracy (0.87–0.91) of the Landsat-derived LULC maps were within the acceptable range. The study results show the existence of spatiotemporal LULC changes resulting in serious forest losses. The analysis revealed that the considerable growth in agricultural land (87.9%) and settlement areas (157%) in the biosphere reserve during the investigation period resulted in a substantial decrease in the area of natural habitats. For instance, the grassland, forest coverage, and wetlands were declined by 74.3%, 30.5%, and 18.1%, respectively. The major drivers for the expansion of agricultural land and settlement areas in the biosphere reserve were the expansion of semi-forest coffee farm investment, human population expansion, resettlement campaigns, and poor follow-up on participatory forest management users. These socio-ecological drivers caused a decline in the proportion of natural habitat areas coupled with a decline in the quality and quantity of their ecosystem services. Thus, efforts are required to manage the ecosystems of Kaffa biosphere reserve through well-integrated landscape resource planning like zonation between economic development and conservation areas. As a result, the land use maps and other data produced in this study can contribute to the development of sustainable land use planning and biodiversity conservation.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.tfp.2021.100161
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
