Three Decades of Land Cover Change in East Africa

Abstract

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands, Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.

A Socio-Ecological Approach to Understanding How Land Use Challenges Human-Elephant Coexistence in Northern Tanzania

“…The slight increment of 51 ha of forest area between 1999 and 2009 was likely due to a compensatory afforestation and plantation program [7]. Our findings that a large proportion of natural vegetation (152,656 ha in 1989 to 116,382 ha in 2019 or 79% in 1989 to 60% in 2019) was transformed to farmland and settlements are consistent with other studies in eastern Africa [44], indicating that anthropogenic activities are the main driver for LULC changes [8,32]. The total human population of the EWMA was around 47,103 people in 2012, with an average annual growth rate of 3% [45], which might have led to an increased demand for natural resources and land.…”

Detection and Attribution of Lake Victoria’s Water-Level Fluctuations in a Changing Climate

“…The apparent drop in the study domain's vegetative cover from the early 2000s may have been caused by intensification of agricultural activities at the expense of forest cover. East Africa's farmed land had significantly increased (by about 34%) since the early 2000s, resulting primarily from the conversion of open and wooded grasslands and open forests to cropped areas (Bullock et al, 2021). Other possible drivers of the observed land use changes include the clearance of forest land for non-agricultural uses, and unsustainable extraction of forest products (Odada et al, 2004;Song et al, 2018;Waiswa et al, 2015).…”

High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales

“…A number of efforts are underway to address various aspects of this challenge, including work on diagnosing and closing yield gaps (Lobell et al, 2009;Licker et al, 2010;Mueller et al, 2012), expanding and commercializing production (Morris and Byerlee 2009), and to understand (Rulli and D'Odorico 2014;Kehoe et al, 2017;Davis et al, 2020) and mitigate (Estes et al, 2016b) agriculture's ecological impacts. The success of these efforts depends heavily on data that accurately describes the location and characteristics of croplands (Fritz et al, 2015), and, given the rapid pace of agricultural change (Gibbs et al, 2010;Zeng et al, 2018;Bullock et al, 2021), how these are changing from 1 year to the next. Unfortunately, for many regions, existing cropland datasets are inaccurate, and are usually created as once-off or infrequently updated products.…”

“…The deficit of information is due to the fact that in many regions the only source of cropland data are remotely sensed land cover maps, which are prone to error. This is particularly true in Africa (Fritz et al, 2010;Estes et al, 2018), where agricultural changes will be largest and the need for accurate baseline data is thus greatest (Searchinger et al, 2015;Estes et al, 2016b;Bullock et al, 2021), and where the characteristics of croplands exacerbate the error inherent in remote sensing analyses. Half of all fields in Africa's smallholder-dominated agricultural systems are smaller than 1 ha (Lesiv et al, 2019).…”