Three Decades of Land Cover Change in East Africa

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Article Three Decades of Land Cover Change in East Africa

Eric L. Bullock 1,*, Sean P. Healey 1, Zhiqiang Yang 1, Phoebe Oduor 2 , Noel Gorelick 3 , Steve Omondi 2, Edward Ouko 2 and Warren B. Cohen 4

1 US Forest Service, Rocky Mountain Research Station, Ogden, UT 84401, USA; [email protected] (S.P.H.); [email protected] (Z.Y.) 2 Regional Centre for Mapping of Resources for Development, Nairobi 00618, Kenya; [email protected] (P.O.); [email protected] (S.O.); [email protected] (E.O.) 3 Google Switzerland, 8002 Zurich, Switzerland; [email protected] 4 USDA Forest Service, Paciﬁc Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA; hammerﬁ[email protected] * Correspondence: [email protected]; Tel.: +1-603-801-0135

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 Citation: Bullock, E.L.; Healey, S.P.; ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a Yang, Z.; Oduor, P.; Gorelick, N.; 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and Omondi, S.; Ouko, E.; Cohen, W.B. ecoregions most impacted by three decades of human development, both spatially and statistically. Three Decades of Land Cover Change in East Africa. Land 2021, 10, 150. Keywords: land cover change; TimeSync; East Africa; Landsat; statistical inference; development https://doi.org/10.3390/ land10020150

Received: 31 December 2020 1. Introduction Accepted: 25 January 2021 Published: 3 February 2021 East Africa has undergone rapid economic growth and environmental change over the past 30 years, resulting in considerable gains in livelihood, sometimes at the expense

Publisher’s Note: MDPI stays neutral of natural ecosystems. From 1990 to 2020, average life expectancy increased from 45 with regard to jurisdictional claims in to 67 years, while forest cover decreased by 17% [1,2]. Meanwhile, climate change is published maps and institutional afﬁl- changing human-environment interactions in complex and often poorly understood ways. iations. For example, crop productivity and food security issues arise under a changing climate, while woody encroachment may be accelerated in part by carbon fertilization [3–6]. Land cover change in Sub-Saharan and interior East Africa (referred to here as “East Africa” for brevity and consisting of the nations of Kenya, Malawi, Rwanda, Tanzania, Ethiopia, Burundi, Zambia, and Uganda) often involves the conversion of woody natural Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. habitats to less-woody cultivated or developed land cover types. The region contains 60% This article is an open access article of the global uncultivated arable land and a population expected to double by 2050 [7,8]. distributed under the terms and Consequently, land use intensiﬁcation will continue to support the growing population, conditions of the Creative Commons affecting some of the most environmentally important and threatened ecosystems in the Attribution (CC BY) license (https:// world. Approximately 73% of the region is contained in the World Wildlife Fund’s (WWF) creativecommons.org/licenses/by/ list of 200 ecoregions prioritized for conservation, compared to 31% of the continent as a 4.0/). whole [9].

Land 2021, 10, 150. https://doi.org/10.3390/land10020150 https://www.mdpi.com/journal/land Land 2021, 10, 150 2 of 15

Land cover in the region is composed primarily of tropical and subtropical grasslands, shrublands, and savannas. Given that grassland, shrublands, and savannas store only approximately 40% of the carbon sequestered by tropical forests per unit area, these areas have not been the focus of conservation mechanisms aimed at offsetting human carbon emissions [10]. However, their soils are also much less productive than in tropical forests, therefore requiring more area of land conversion to achieve similar agricultural yields [11]. Despite having fewer trees than forests, the region’s savannas still contain approximately 5.5 PgC in aboveground carbon [12]. This carbon storage is at risk as pressures toward agricultural and urban expansion persist. However, there is evidence that additional carbon sequestration is occurring in the region through woody expansion in grasslands, caused in part by increasing atmospheric CO2, fires, changing hydrologic conditions, and shifting ecozones [4,13]. Using L-band radar analysis, McNicol et al. (2018) found that between 2007 and 2010, carbon gain in Sub-Saharan woodlands largely counteracted loss, due to degradation and deforestation. Brandt et al. (2017) used passive remote sensing to reach a similar conclusion, finding an increase in woody cover between 1992 and 2011 in Sub-Saharan Africa, largely in drylands [14]. Objective monitoring of trends among the Region’s land cover classes (which here include Cropland, Dense and Open Forests, Vegetated Wetland, Wooded and Open Grasslands, Settlements, Open Water, and Other) is needed to reconcile these results with rapid population growth and dependence on fuelwood for the Region’s energy [15]. Remote sensing represents the most feasible way of obtaining land cover information in a temporally and spatially consistent way. To date, most studies on land use and cover change in the region have been limited to coarse resolution analysis (e.g., Reference [16]), smaller spatial domains (e.g., References [17–19]), or have involved only change affecting a particular cover type, such as forests [20,21]. The limited spatial domains and/or change classes analyzed in these studies prevent the comprehensive and regional assessment of trends in land cover change. Here, we seek to fill this information gap by analyzing long-term (30 years) land cover trends using a legend, spatial domain, and time period that reflect the region’s ecological diversity and gradual socioeconomic and climatic changes. A tool that has proven appropriate for coherent land change research is TimeSync, which provides users with a platform for interpreting historical Landsat data to record land change dynamics at a designed statistical sample of reference locations [22,23]. International “Good Practice” guidance for monitoring land change recommends using such a sample with an unbiased statistical estimator [24–26]. This allows consistent estimates of the land cover distribution of an area of interest over time and with straightforward measures of uncertainty. Area estimation through formal inference using a reference sample has proven ad- vantages beyond simple map-based approaches to estimating area (i.e., “pixel counting”). The primary disadvantage to “pixel counting” is that map errors can introduce a substantial bias to the estimates of area, which is particularly true when evaluating change in complex landscapes like in East Africa [24–26]. Implementing a designed sample of high-quality observations allows for using unbiased statistical estimators and straightforward uncertainty estimation. While this approach does not generate a high-resolution map of land cover change, changes observed across a sampling grid can nevertheless be used with kernel-based “heat map” approaches to understanding sub-regional spatial patterns of change. This research is aimed to address the following research question: What are the primary land cover and land use trends in East Africa over the previous 30 years, and how do they vary by country and ecoregion? To address this question, we performed a robust regional assessment using TimeSync as implemented by regional land cover experts. LandLand2021 2021, 10, 10, 150, x FOR PEER REVIEW 33 of of 15 16

2.2. Methodology Methodology 2.1.2.1. Study Study Area Area WeWe analyzed analyzed trends trends from from 1988 1988 to 2017 to in2017 the in East the African East nationsAfrican ofnations Uganda, of Rwanda,Uganda, Tanzania,Rwanda, Malawi,Tanzania, Zambia, Malawi, Kenya, Zambia, and Kenya, Ethiopia and (FigureEthiopia1). (Figure Predominant 1). Predominant ecoregions ecore- in thegions study in the area study include area the include miombo the miombo woodlands woodlands in Zambia, in Zambia, Tanzania, Tanzania, and Malawi, and Malawi, dense tropicaldense tropical forests forests in Rwanda in Rwanda and Uganda, and Uganda, montane montane shrublands shrublands in Ethiopia, in Ethiopia, and subtrop- and sub- icaltropical savannas. savannas. Apart Apart from from Rwanda Rwanda in the in 1990s, the 1990s, each countryeach country has undergone has undergone relatively rela- consistenttively consistent economic economic and population and population growth (Worldgrowth Bank, (World 2014). Bank, To 2014). support To this support growing this populationgrowing population and to combat and to a recentcombat legacy a recent of foodlegacy insecurity, of food insecurity, the region the is activelyregion is expand- actively ingexpanding and intensifying and intensifying agricultural agricultural activity. activity. The three-decade The three-decade study periodstudy period was chosen was cho- to correspondsen to correspond to the maximum to the maximum time of socioeconomic time of socioeconomic growth with growth consistent with consistent reference datarefer- necessaryence data for necessary the analysis for the (described analysis below).(described below).

FigureFigure 1. 1. The ecoregions of of the the seven-country seven-country study study re regiongion in inEast East Africa. Africa. The The 2000 2000 randomly randomly selected sample units are shown for Tanzania, however the same sampling routine was performed selected sample units are shown for Tanzania, however the same sampling routine was performed in each country. Sh, Shrublands; Gr, Grasslands; Sa, Savannas; Fo, Forests; T&S, Tropical and Sub- in each country. Sh, Shrublands; Gr, Grasslands; Sa, Savannas; Fo, Forests; T&S, Tropical and tropical. Land covers are defined by ecoregion categorization in Olson et al. (2001). Subtropical. Land covers are deﬁned by ecoregion categorization in Olson et al. (2001). 2.2.2.2. Area Area Estimation Estimation LandLand cover cover areas areas were were estimated estimated using using observations observations of of land land cover cover history history at at a a simple simple randomrandom sample sample of of 2000 2000 locations locations in in each each country. country. The The spatial spatial and and spectral spectral resolution resolution of of LandsatLandsat imagery, imagery, in in addition addition to to its its consistent consistent availability availability back back in in time, time, make make it it ideal ideal for for thisthis application. application. Sample Sample points points were were interpreted interpreted by by local local experts experts using using the theTimeSync TimeSync soft- softwareware [22], [22 accessing], accessing historical historical Landsat Landsat and and high-resolution high-resolution imagery. AA landland covercover label label waswas assigned assigned to to each each sample sample unit unit for for every every year year from from 1988 1988 to 2017to 2017 by evaluatingby evaluating the the sample sam- locationple location against against all available all available Landsat Landsat data and data high-resolution and high-resolution imagery imagery on Google on Google Earth. TheEarth. reference The reference labels represent labels represent sub-classes sub-classes to the International to the International Panel on Panel Climate on Change Climate (IPCC)Change top-level (IPCC) top-level land use categories:land use categories: Open Forest, Open Dense Forest, Forest, Dense Cropland, Forest, Cropland, Settlements, Set- Woodedtlements, Grassland, Wooded Grassland, Open Grassland, Open VegetatedGrassland,Wetland, Vegetated Open Wetland, Water, Open and OtherWater, land and (deﬁnitionsOther land given(definitions in Reference given in [27 Reference], and elaborated [27], and in elaborated Table1). in Table 1). Land 2021, 10, 150 4 of 15

Table 1. Land cover class deﬁnitions are derived as sub-classes from the International Panel on Climate Change (IPCC) top-level land use categories.

Land Cover Class Definition Tree-covered areas with 15–40% canopy cover. This class includes both natural and planted forests Open Forest that meet the canopy cover threshold and the national definitions of forest cover. Tree-covered areas with over 40% canopy cover. This class includes both natural and planted forests Dense Forest that meet the canopy cover threshold. An area primarily used for agriculture. Includes field crops, agroforestry, horticulture, perennial Cropland crops and floriculture, and abandoned croplands left fallow. Settlements Areas primarily built for the development of buildings, roads, or other human-modified land uses. A natural landscape that does not meet the tree canopy thresholds of either forest class and contains Wooded Grassland woody vegetation in the form of shrubs, very sparse tree cover, or single trees. A natural landscape that does not meet the tree canopy thresholds of either forest class and does not containing substantial woody vegetation. The plant cover is composed principally of grasses, Open Grassland grass-like plants, and forbs, including areas where practices such as clearing, burning, chaining, and/or chemicals are applied to maintain the grass vegetation. Areas covered in water for at least part of the year and containing a mix of vegetation and open water. Vegetated Wetland Includes marshlands, swamps, and peatlands. Open Water Open water bodies, including oceans, lakes, rivers, and reservoirs. Areas that do not meet a different class definition, including barren land, rock outcrops, permanent Other land snow cover, beaches, and salt crusts.

The reference samples informed our analysis in two ways. First, the samples were used as the basis for clustering using weighted kernel density analysis. Clustering was performed in Python using the ‘KernelDensity’ estimator (parameters: bandwidth = 0.2, kernel = gaussian, algorithm = ball_tree, additional parameters = default) in the Scikit-learn module [28]. The purpose of this analysis was to determine and visualize “hotspots” of important land use dynamics. To account for differences in country sizes, each sample unit was given a weight based on the proportional size of the country containing the sample. For each sample unit n in country c, the weight was calculated as:

ac wc,n = 1 ∗ ( ) amax where, wc,n = sample weight for sample unit n and country c

ac = Area of country c

amax = Maximum area of countries in study region The second use of the reference samples was to statistically estimate areas of land cover and change at various spatial scales using statistical inference. Areas were estimated at the country, regional, and ecoregion scale. For each scale of analysis, it was necessary to properly account for the inclusion probability of each sample unit for the corresponding spatial domain. The samples were selected at the country scale. Therefore, the inclusion probability of each sample unit is directly dependent on the area of the country. For estimation at the country scale, each sample unit has the same inclusion probability, and the area of classes of interest can be estimated directly based on the proportion of each class within the reference sample [29]. In this context, areas and standard errors can be calculated using a simple expansion estimator, with the implementation used here deﬁned in Equations (29) and (30) in Reference [30]. To estimate areas across the entire seven-country study region, the expansion estimator could not be utilized, since the inclusion probability differs for sample units of different countries. To account for varying inclusion probabilities, we deployed a stratiﬁed estimator Land 2021, 10, 150 5 of 15

(Equations (31) and (32) in Reference [30]) for the regional analysis in which the area weights of each sample unit were computed from the size of each country. For estimation within ecoregions, which cross national boundaries and contain differing sample subsets, the ratio estimator described in Stehman (2014) was used [31].

3. Results Our analysis revealed 29,040,000 (±1,950,000) ha of land cover change from 1988 to 2017, representing 7.7% of the study region (Figure2). Proportionally, the areas of Cropland and Settlement increased by 35% and 43%, respectively, while all other land use classes Land 2021, 10, x FOR PEER REVIEW decreased (Figure3). In 2017, the dominant land cover in the region was Wooded Grassland,6 of 16 which, despite a 7.0% decrease over the study period, still made up 36% of the study area, or 134,400,000 (±3,710,000) ha. Altogether, there was a decline of 18,940,000 (±1,600,000) ha naturally vegetated land uses (Grasslands, Forests, and Vegetated Wetland).

Figure 2. A Sankey diagram, showing land use transitions between 1988 (left) and 2017 (right). Total change class area (e.g., loss of Open Forest) corresponds to the size of the class nodes, while the link width corresponds to the area of Figure 2. A Sankey diagram, showing land use transitions between 1988 (left) and 2017 (right). Table 3. Yearly change in conversion between classes. Note that the largest links (or greatest area of conversion) exist between the conversion of the three classes with the greatest overall change (top), and proportional change in land cover classes across the study domainWooded relative Grassland, to the Open area Grass-land,in 1988 (bottom). and Open Standard Forest errors to Cropland. are expressed as vertical lines in the plots of yearly estimated areas. Land cover change as a proportion of land area was greatest in Uganda (5.29% ± 1.54) and Malawi (4.21% ± 1.56), and lowest in Kenya (0.86% ± 0.47) and Ethiopia (2.49% ± 1.38) (Figure4). Land cover conversion in Uganda was greatly accelerated after 2000, after which there was a notable increase in Cropland and reduction in Open and Wooded Grasslands. Over the 30-year study period, the most common land cover in Uganda switched from Open Grassland to Cropland, while in Zambia, it switched from Open Forest to Open Grassland. No other country changed the primary land cover composition. The land covers with signiﬁcant changes in the area are detailed below.

Figure 3. Yearly change in the three classes with the greatest overall change (top), and proportional change in land cover classes across the study domain relative to the area in 1988 (bottom). Standard errors are expressed as vertical lines in the plots of yearly estimated areas. Land 2021, 10, x FOR PEER REVIEW 6 of 16

Figure 2. A Sankey diagram, showing land use transitions between 1988 (left) and 2017 (right). Table 3. Yearly change in Land 2021the ,three10, 150 classes with the greatest overall change (top), and proportional change in land cover classes across the study 6 of 15 domain relative to the area in 1988 (bottom). Standard errors are expressed as vertical lines in the plots of yearly estimated areas.

Land 2021, 10, x FOR PEER REVIEW 7 of 16

Land cover change as a proportion of land area was greatest in Uganda (5.29% ± 1.54) and Malawi (4.21% ± 1.56), and lowest in Kenya (0.86% ± 0.47) and Ethiopia (2.49% ± 1.38) (Figure 4). Land cover conversion in Uganda was greatly accelerated after 2000, after which there was a notable increase in Cropland and reduction in Open and Wooded Grasslands. Over the 30-year study period, the most common land cover in Uganda FigureFigure 3. Yearly 3. Yearly change change in the inthree the classes three classes with the with greatest the greatest overall overall change change (top), and (top), pr andoportional proportional change change in land in cover land classes cover acrossclasses the study across domain the study relative domain toswitched the relative area fromin to1988 the Open (bottom). area Grassland in 1988 Standard (bottom). to Cropland,errors Standard are expr wh errorsessedile in are as Zambia, expressedvertical itlines switched as verticalin the plots from lines of Open in yearly the For- estimatedplots areas. of yearly estimated areas.est to Open Grassland. No other country changed the primary land cover composition. The land covers with significant changes in the area are detailed below.

FigureFigure 4. The 4. yearlyThe yearly proportion proportion of land of cover land cover classes classes for each for eachcountr countryy in the in study the study region. region. Note Notethat Uganda that Uganda and Zambia and Zambia changed in thechanged land cover inthe representing land cover the representing largest proportion the largest of proportionthe country. of the country.

3.1. Conversion to Croplands In total, there was 18,154,000 (±1,580,000) ha, or 34.8%, increase in Cropland. Accord- ing to our sample, the development of Cropland was most prevalent in Open Grasslands, followed by Wooded Grassland and Open Forests. Primary hotspots for the conversion of Cropland in addition to the largest proportional increases occurred in Zambia (106%), Uganda (55%), and Tanzania (53%), while the largest overall area increase was in Tanza- nia (64,10,000 ha). In the last decade of our analysis, the fastest growth in Cropland occurred in Uganda (0.4%/year) and Zambia (0.27%/year). The highest agricultural conversion rate occurred in the Central Zambezian Miombo Woodlands (±5,940,000 ha), Victoria Basin Forest-Savanna Mosaic (±1,980,000 ha), Southern Acacia-Commiphora Bushlands and Thickets (±1,600,000 ha), East Sudanian Savanna (±1,300,000 ha), and the Eastern Mi- ombo Woodlands (±1,100,000 ha) (Figure 5). Land 2021, 10, 150 7 of 15

3.1. Conversion to Croplands In total, there was 18,154,000 (±1,580,000) ha, or 34.8%, increase in Cropland. Accord- ing to our sample, the development of Cropland was most prevalent in Open Grasslands, followed by Wooded Grassland and Open Forests. Primary hotspots for the conversion of Cropland in addition to the largest proportional increases occurred in Zambia (106%), Uganda (55%), and Tanzania (53%), while the largest overall area increase was in Tanzania (64,10,000 ha). In the last decade of our analysis, the fastest growth in Cropland occurred in Uganda (0.4%/year) and Zambia (0.27%/year). The highest agricultural conversion rate occurred in the Central Zambezian Miombo Woodlands (±5,940,000 ha), Victoria Basin Forest-Savanna Mosaic (±1,980,000 ha), Southern Acacia-Commiphora Bushlands and Land 2021, 10, x FOR PEER REVIEWThickets (±1,600,000 ha), East Sudanian Savanna (±1,300,000 ha), and the Eastern Miombo8 of 16

Woodlands (±1,100,000 ha) (Figure5).

Figure 5. Cropland gain in East Africa. Left: Kernel Density hotspot analysis of new cropland. Top, proportional change Figure 5. Cropland gain in East Africa. Left: Kernel Density hotspot analysis of new cropland. Top, proportional change of of Cropland in seven countries. Bottom, Proportional change of Cropland in ecoregions in which the five ecoregions Croplandwith the ingreatest seven countries.change are Bottom, colored Proportional (CZMW, Central change Za ofmbezian Cropland Miombo in ecoregions Woodlands, in which ESS, theEast ﬁve Sudanian ecoregions Savanna, with the greatestSMW, changeSouthern are Miombo colored Woodlands, (CZMW, Central SABAT, Zambezian Southern Miombo Acacia-Commiphora Woodlands, ESS, Bushlands East Sudanian and Thicklets, Savanna, VBFM, SMW,Southern Victoria MiomboBasin Forest-Savanna Woodlands, SABAT, Mosaic). Southern The grey Acacia-Commiphora lines represent the Bushlands other ecoregions. and Thicklets, VBFM, Victoria Basin Forest-Savanna Mosaic). The grey lines represent the other ecoregions. 3.2. Settlements Expansion 3.2. SettlementsThe area Expansionof Settlements grew by 460,000 (±250,000) ha, or 43.5%, over the study period. Settlements increased in every country, but increased the most in Uganda, Rwanda, The area of Settlements grew by 460,000 (±250,000) ha, or 43.5%, over the study and Ethiopia, with smaller hotspots in Tanzania and southern Malawi (Figure 6). The pri- period. Settlements increased in every country, but increased the most in Uganda, Rwanda, mary land use replaced by Settlements for the region was Open Grassland, however the and Ethiopia, with smaller hotspots in Tanzania and southern Malawi (Figure6). The pri- marypatterns land were use replaceddifferent by in Settlementsindividual countries for the region (Figure was 7). Open In Rwanda, Grassland, Settlements however were the patternsdeveloped were in differentWooded inGrasslands individual and countries Cropland, (Figure while7). in In Kenya, Rwanda, they Settlements were the result were of developeddeforestation in Wooded of Dense Grasslands Forest. and Cropland, while in Kenya, they were the result of deforestation of Dense Forest. Land 2021, 10, 150 8 of 15 Land 2021, 10, x FOR PEER REVIEW 9 of 16

Land 2021, 10, x FOR PEER REVIEW 9 of 16

FigureFigure 6. Settlement 6. Settlement gain gain in East in East Africa. Africa. Left: Left: Kernel Kernel Density Density hotspot hotspot analysis analysis of new of new Settlements. Settlements. Top, Top, proportional proportional change change of Set- tlements in seven countries. Bottom, Proportional change of Settlements in ecoregions in which the five ecoregions with the great- Figureof 6. Settlements Settlement ingain seven in East countries. Africa. Bottom,Left: Kernel Proportional Density hotspot change an ofalysis Settlements of new inSettlements. ecoregions Top, in which proportional the ﬁve ecoregionschange of Set- est change are colored (CZMW, Central Zambezian Miombo Woodlands, VBFM, Victoria Basin Forest-Savanna Mosaic, SZCFM, tlementswith in the seven greatest countries. change Botto are coloredm, Proportional (CZMW, Centralchange Zambezianof Settlements Miombo in ecoregions Woodlands, in which VBFM, th Victoriae five ecoregions Basin Forest-Savanna with the great- Southern Zanzibar-Inhambane Coastal Forest Mosaic, NZCFM, Northern Zanzibar-Inhambane coastal forest mosaic, EMGAW, est change are colored (CZMW, Central Zambezian Miombo Woodlands, VBFM, Victoria Basin Forest-Savanna Mosaic, SZCFM, EthiopianMosaic, montane SZCFM, grasslands Southern and Zanzibar-Inhambane woodlands). The grey Coastal lines Forest represent Mosaic, the NZCFM,other ecoregions. Northern Zanzibar-Inhambane coastal Southernforest Zanzibar-Inhambane mosaic, EMGAW, Ethiopian Coastal Forest montane Mosaic, grasslands NZCFM, and Northern woodlands). Zanzibar-Inhambane The grey lines represent coastal theforest other mosaic, ecoregions. EMGAW, Ethiopian montane grasslands and woodlands). The grey lines represent the other ecoregions.

Figure 7. Previous cover type of land converted to Cropland (left) and Settlements, as a proportion of observed conversions per country. FigureFigure 7. Previous 7. Previous cover covertype of type land of converted land converted to Cropland to Cropland (left) ( leftand) andSettlements, Settlements, as a asproportion a proportion of observed of observed conversions conversions per country. per country. 3.3. Deforestation Deforestation of Open Forest occurred most frequently in Zambia and Tanzania and 3.3.3.3. Deforestation Deforestation resulted in a 13.5%, or 7,940,000 (±1,070,000) ha, decrease in Open Forests. The ecoregion Deforestation of Open Forest occurred most frequently in Zambia and Tanzania and withDeforestation the greatest ofdecrease Open Forestin Open occurred Forests most was frequentlythe Central in Zamb Zambiaezian and Miombo Tanzania Wood- and resulted in a 13.5%, or 7,940,000 (±1,070,000)± ha, decrease in Open Forests. The ecoregion resultedlands (Figure in a 13.5%, 8). Dense or 7,940,000 Forests ( are1,070,000) much less ha, common decrease inthan Open Open Forests. Forests The in ecoregion the study withwith the greatestgreatest decreasedecrease in in Open Open Forests Forest wass was the the Central Central Zambezian Zambezian Miombo Miombo Woodlands Wood- domain, and therefore, deforestation in Dense Forests was also much less frequent, de- lands(Figure (Figure8). Dense 8). Dense Forests Forests are much are less much common less common than Open than Forests Open inForests the study in the domain, study creasing 950,000 (±330,000) ha. Deforestation in Dense Forests occurred most frequently domain,and therefore, and therefore, deforestation deforestation in Dense in Forests Dense wasForests also was much also less much frequent, less frequent, decreasing de- in Uganda and was driven largely by conversion to Open and Closed Grasslands and creasing 950,000 (±330,000) ha. Deforestation in Dense Forests occurred most frequently in Uganda and was driven largely by conversion to Open and Closed Grasslands and Land 2021, 10, 150 9 of 15

Land 2021, 10, x FOR PEER REVIEW 10 of 16 950,000 (±330,000) ha. Deforestation in Dense Forests occurred most frequently in Uganda Land 2021, 10, x FOR PEER REVIEW and was driven largely by conversion to Open and Closed Grasslands and Cropland10 of 16 (Figure9). Despite substantial Dense Forest in Rwanda, no loss of such forests was Cropland (Figure 9). Despite substantial Dense Forest in Rwanda, no loss of such forests recorded (1988–2017) in the 2000 study plots in that country. was recorded (1988–2017) in the 2000 study plots in that country. Cropland (Figure 9). Despite substantial Dense Forest in Rwanda, no loss of such forests was recorded (1988–2017) in the 2000 study plots in that country.

Figure 8. Deforestation in Open Forests in East Africa. Left: Kernel Density hotspot analysis of deforestation in Open Forests. Top, Figure 8. Deforestation in Open Forests in East Africa. Left: Kernel Density hotspot analysis of deforestation in Open proportional change of Open Forest in seven countries with 95% confidence intervals expressed as vertical lines. Bottom, Propor - Forests. Top, proportional change of Open Forest in seven countries with 95% conﬁdence intervals expressed as vertical tional change of Open Forest in ecoregions in which the five ecoregions with the greatest change are colored (CZMW, Central Zam- Figurelines. 8. Deforestation Bottom, Proportional in Open Forests change in of East Open Africa. Forest Left: in ecoregionsKernel Dens inity which hotspot the ﬁveanalysis ecoregions of deforestation with the greatest in Open change Forests. are Top, proportionalbezian Miombo change Woodlands, of Open Forest VBFM, in seVictoriaven countries Basin Forest-Savanna with 95% confidence Mosaic, intervals EMF, Ethiop expressedian montane as vertical forests, lines. SMW, Bottom, Southern Propor mi-- colored (CZMW, Central Zambezian Miombo Woodlands, VBFM, Victoria Basin Forest-Savanna Mosaic, EMF, Ethiopian tionalombo change woodlands, of Open NZCFM, Forest in Northern ecoregions Zanzibar-Inhambane in which the five ecoregions coastal fo restwith mosaic). the grea Thetest greychange lines are represent colored (CZMW, the other Central ecoregions. Zam - montane forests, SMW, Southern miombo woodlands, NZCFM, Northern Zanzibar-Inhambane coastal forest mosaic). bezian Miombo Woodlands, VBFM, Victoria Basin Forest-Savanna Mosaic, EMF, Ethiopian montane forests, SMW, Southern mi- omboThe woodlands, grey lines NZCFM, represent Northern the other Zanzibar-Inhambane ecoregions. coastal forest mosaic). The grey lines represent the other ecoregions.

Figure 9. The proportion of land cover area after deforestation of Dense Forest (left) and Open Forest (right).

FigureFigure 9. 9. TheThe proportion proportion3.4. of of landWooded land cover cover Grasslands area area after after deforestation deforestation of of Dense Dense Forest Forest ( (leftleft)) and and Open Open Forest Forest ( (rightright).). There were 12,170,000 (±1,330,000) ha of Wooded Grasslands converted to other land 3.4.cover Wooded classes Grasslands and 1,880,000 (±510,000) ha gain of Wooded Grasslands, resulting in a net changeThere of were 7.0%. 12,170,000 Most (81%) (±1,330,000) of the conversion ha of Wooded to Wooded Grasslands Grasslands converted occurred to other in Open land or coverClosed classes Forests, and 1,880,000with the remaining(±510,000) occurringha gain of in Wooded Open Grasslands Grasslands, (14%) resulting and Croplandsin a net change(5%). ofThe 7.0%. largest Most proportional (81%) of the decrease conversion in ecoregions to Wooded occurred Grasslands in the occurred Central in Zambezian Open or Closed Forests, with the remaining occurring in Open Grasslands (14%) and Croplands (5%). The largest proportional decrease in ecoregions occurred in the Central Zambezian Land 2021, 10, 150 10 of 15

3.4. Wooded Grasslands There were 12,170,000 (±1,330,000) ha of Wooded Grasslands converted to other land cover classes and 1,880,000 (±510,000) ha gain of Wooded Grasslands, resulting in a net Land 2021, 10, x FOR PEER REVIEWchange of 7.0%. Most (81%) of the conversion to Wooded Grasslands occurred in Open11 of or 16

Closed Forests, with the remaining occurring in Open Grasslands (14%) and Croplands (5%). The largest proportional decrease in ecoregions occurred in the Central Zambezian Miombo Woodlands, and by country was in Uganda (Figure 10). The only ecoregion with a Miombo Woodlands, and by country was in Uganda (Figure 10). The only ecoregion with net gain was the Zambezian Cryptosepalum dry forests; every other ecoregion was stable a net gain was the Zambezian Cryptosepalum dry forests; every other ecoregion was sta- or decreasing in area. Conversion of Woody Grassland was predominantly for Cropland, ble or decreasing in area. Conversion of Woody Grassland was predominantly for and to a lesser extent, Open Grassland (Figure2). Cropland, and to a lesser extent, Open Grassland (Figure 2).

Figure 10. Heatmaps of loss and gain of Wooded Grassland. Left: Kernel Density hotpot analysis of loss and gain of Wooded Figure 10. Heatmaps of loss and gain of Wooded Grassland. Left: Kernel Density hotpot analysis of loss and gain of Grasslands. Top: Proportional change of Wooded Grasslands in seven countries. Bottom: Proportional change of Wooded Grass- Wooded Grasslands. Top: Proportional change of Wooded Grasslands in seven countries. Bottom: Proportional change lands in ecoregions in which the five ecoregions with the greatest change are colored (CZMW, Central Zambezian Miombo Wood- lands,of SMW, Wooded Southern Grasslands miombo in ecoregions woodlands, in ESS, which East the Sudanian five ecoregions savanna, with VBFM, the greatest Victorian change Basin areforest-savanna colored (CZMW, mosaic, Central SABAT, SouthernZambezian Acacia-Commiphora Miombo Woodlands, Bushlands SMW, and Southern Thicklets) miombo. The woodlands,grey lines represent ESS, East the Sudanian other ecoregions. savanna, VBFM, Victorian Basin forest-savanna mosaic, SABAT, Southern Acacia-Commiphora Bushlands and Thicklets). The grey lines represent the other ecoregions. 3.5. Cross-Class Woody Vegetation Change The natural vegetation cover classes considered here contain a continuum of woody 3.5.plant Cross-Class density. WoodySpecifically, Vegetation classes Change in descending order of woody vegetation are: Dense ForestThe > naturalOpen Forest vegetation > Wooded cover Grassland classes considered > All other here classes. contain This a continuum succession of provides woody plantuseful density. context Specifically, for our estimates classes of in cover descending class change. order ofIn woodyisolation, vegetation an increase are: in Dense Open ForestForests > might Open Forestbe considered > Wooded a positive Grassland development > All other unless classes. most This new succession Open Forests provides come usefulfrom the context degradation for our estimatesof Dense Forest of cover (which class is change. the case, In regionally). isolation, an If increasewe define in woody Open Forestsvegetation might change be considered across classes a positive according development to the above unless order, most our new sample OpenForests suggests come that fromsuch thechange degradation has been of dominated Dense Forest by changes (which isresulting the case, in regionally). fewer trees If and we shrubs. define woodySpecifi- vegetationcally, we estimate change acrossthat changes classes involving according reduced to the above woody order, vegetation our sample affected suggests 20,930,000 that such(± 2,090,000) change has ha in been the dominated region, while by changes resulting in fewermore wood trees andoccurred shrubs. on Specifi-940,000 cally,(± 360,000) we estimate ha. Out that of the changes 6,610,000 involving (± 980,000) reduced ha change woody in vegetation natural vegetation affected classes 20,930,000 (i.e., (±between2,090,000) non-developed ha in the region, classes, while including changes grasslands, resulting in wetlands, more wood and occurred forests), on5,770,000 940,000 (± (±1,150,000)360,000) resulted ha. Out in of a the decrease 6,610,000 in woody (± 980,000) vegetation ha change (Figure in natural11). Nowhere vegetation did the classes area (i.e.,of woody between increase non-developed exceed woody classes, decrease including be grasslands,tween natural wetlands, or human- andmodified forests), 5,770,000 classes. (± 1,150,000) resulted in a decrease in woody vegetation (Figure 11). Nowhere did the area of woody increase exceed woody decrease between natural or human-modified classes. Land 2021, 10, 150 11 of 15 Land 2021, 10, x FOR PEER REVIEW 12 of 16

Figure 11. Hotspot analysis of land cover conversion resulting in a reduction in woody vegetation class (left) and increasing (right). Figure 11. Hotspot analysis of land cover conversion resulting in a reduction in woody vegetation class (left) and increasing (right). 4. Discussion 4. DiscussionEast Africa has undergone extensive environmental change in the past three decades, largely driven by the expansion of Cropland and the conversion of naturally vegetated East Africa has undergone extensive environmental change in the past three decades, land covers. From 1988 to 2017, we found that the area of Cropland and Settlements in- largely driven by the expansion of Cropland and the conversion of naturally vegetated creased by 35% and 43%, respectively, driving large-scale reductions in woody vegetation. land covers. From 1988 to 2017, we found that the area of Cropland and Settlements While previously mentioned studies reported substantial increases in woody biomass se- increased by 35% and 43%, respectively, driving large-scale reductions in woody vegetation. questration in the region’s ecosystems, land cover monitoring suggests a trend in the op- While previously mentioned studies reported substantial increases in woody biomass positesequestration direction. in the region’s ecosystems, land cover monitoring suggests a trend in the oppositeA first-order direction. approximation of an area’s carbon dynamics that is commonly used in internationalA first-order reporting approximation mechanisms of (Penman an area’s et carbon al., 2003) dynamics involves thatthe calculation is commonly of emis- used sionin international factors associated reporting with mechanisms specific land (Penman cover conversions, et al., 2003) which involves are thethen calculation used with ofa recordemission of such factors conversions associated (called with specific“activity land data”) cover to infer conversions, changes in which net carbon are then storage. used Itwith is outside a record the of scope such conversionsof this paper (calledto assess “activity whether data”) within-class to infer changes in the net Region carbon meritstorage. use It of is outsidean evolving the scope emis ofsion this factor paper for to assessthe loss whether of natural within-class woody cover, changes but in the changesRegion merit we document use of an have evolving clear emission carbon implications. factor for the McNicol loss of natural et al. (2018) woody calculated cover, but that the deforestationchanges we document in woodlands have in clear the Region carbon resulted implications. in a carbon McNicol decrease et al. from (2018) 14 calculated ± 4 to 6 ± 5that MgC deforestation ha−1, with deforestation in woodlands occurring in the Region most resulted frequently in a carbonin low-biomass decrease parts from 14of ±the4 landscape.to 6 ± 5 MgC Applying ha−1, withthis estimate deforestation as an emis occurringsion factor most to frequently our results in would low-biomass find approx- parts imatelyof the landscape. 0.015 PgC Applying loss from this conversion estimate of as Wooded an emission Grasslands factor to over our resultsour study would period. find Whileapproximately there were 0.015 also PgC gains loss in fromWooded conversion Grasslands, of Wooded it was primarily Grasslands at overthe expense our study of Openperiod. Forests, While thereand therefore, were also gainswould in not Wooded represent Grasslands, substantial it was carbon primarily gains. at theFuture expense re- searchof Open can Forests, build on and our therefore,results through would more not de representtailed carbon substantial accounting carbon to quantify gains. Future landscape-levelresearch can carbon build dynamics. on our results through more detailed carbon accounting to quantify landscape-levelAll five of the carbon ecoregions dynamics. with the greatest increase in Cropland were savannas or woodlands. The ecoregion containing the largest proportion of land cover change was the Land 2021, 10, 150 12 of 15

All five of the ecoregions with the greatest increase in Cropland were savannas or woodlands. The ecoregion containing the largest proportion of land cover change was the Central Zambezian Miombo Woodlands. Due in part to its poor soils, the Zambezian Woodlands has remained largely intact and has consequently become a hotspot of biodiversity [11]. Of the global ecoregions, it contains the third-highest mammal species richness and 17th in floral diversity [32,33]. The Zambezian Woodlands are also located in the heart of the “savannah transformation frontier”, with a rapidly growing population and increasing pressure from poaching, fuel harvesting, and agriculture [11]. This trend is emblematic of Sub-Saharan Africa as a whole: Growing demand for food is forcing agricultural expansion in historically less developed savannas and woodlands. Every country in our study region saw the expansion of Settlements, with the largest proportional increase occurring in Rwanda. D’Amour et al. (2017) predicted that Rwanda, which has the highest population density of any continental African country, will lose 34% of its cropland, due to urbanization by 2030, more than any other country besides Egypt [34]. Currently, an estimated 31% of the nation’s GDP originates from the agricultural sector, which suggests that economic and societal rearrangement would be necessary for large- scale urbanization in croplands [35]. Our results did reveal 10000 ha of Cropland converted to Settlements in Rwanda, proportionally the largest of any country (Figure7) . However, we found 10 times more area of new Cropland, resulting in a net gain of 97,000 ha. Any loss of agricultural land near settlements is still being more than offset by new cultivation of former grasslands and woodlands. Open Forests had the greatest proportional decrease of any class in the study region. In Ethiopia, Tanzania, Malawi, Uganda, and Zambia, deforestation of Open Forests primarily resulted in conversion to Cropland and Open Grassland. We hypothesize that conversion to Open Forest is due to timber and fuelwood harvest, although further investigation is needed to support this theory. Alternative drivers of deforestation in the region besides agriculture and development include poaching, hunting, and fire management. Interestingly, no conversion of Dense Forestland was detected in our Rwandan sample. While Dense Forest cover loss has indeed occurred in the country, particularly during times of conflict [36], the fact that no such conversion was detected across 2000 randomly located plots suggests that remaining Dense Forests are restricted to effectively protected reserves. In 2017, we estimate that 23.5% of the study region was anthropogenically modified in the form of Cropland or Settlements. Jacobson et al. (2015) found a similar study region to be 29.8% anthropogenically developed. The discrepancy can likely be attributed to differences in class definitions: Jacobson assigned any visual sign of anthropogenic land cover to that class, while we assigned labels based on dominant land cover. Our study adds important historical context, as we found 26.4% of the area of Cropland and Settlements in 2017 was converted from natural vegetation since 1988. In other words, over a quarter of anthropogenic land cover conversion in East Africa has occurred since 1988. Our analysis revealed three primary hotspots of land cover change in the region: Central Zambia, north-central Tanzania, and central Uganda (Figure 12). This information can be used to guide conservation initiations and mitigate unsustainable land cover changes in the future. Targeted research on the underlying socioeconomic drivers of conversion in these hotspots is needed to enact policies that can reduce the impact of development-driven land change on the environment. The fact that these hotspots occur primarily in savannas and woodlands supports the theory of a “savannah transformation frontier” described in Estes et al. (2016). Land 2021, 10, 150 13 of 15 Land 2021, 10, x FOR PEER REVIEW 14 of 16

Figure 12. Three primary hotspots of land cover conversion, as calculated using kernel density Figure 12. Three primary hotspots of land cover conversion, as calculated using kernel density estimation. This figure combines each type of land cover conversion analyzed in this study. estimation. This figure combines each type of land cover conversion analyzed in this study. Our use of a designed sample of expert image interpretations calculates the confi- Our use of a designed sample of expert image interpretations calculates the confidence intervalsdence intervals for all our for estimates all our estimates in a straight-forward in a straight-forward manner. While manner. our While estimates our represent estimates therepresent most spatially the most and spatially temporally and consistenttemporally assessments consistent ofassessments the region’s of land the coverregion’s trends land tocover date, trends our confidence to date, our intervals confidence indicate intervals how well indicate our sample how well of 2000 our random sample samples of 2000 perran- countrydom samples addresses per specific country classes addresses of interest. specific For classes example, of interest. the margin For ofexample, errors (defined the margin as theof errors ratio of (defined the half-width as the ratio of the of 95%the half-wid confidenceth of intervals the 95% toconfidence the estimate intervals and expressed to the esti- heremate as and a percentage) expressed forhere our as Settlementa percentage) and for Cropland our Settlement change and classes Cropland are 54.3% change and classes 8.7%, respectively,are 54.3% and indicating 8.7%, respectively, much higher indicating precision inmuch the estimatehigher precision of Cropland in the change. estimate This of uncertaintyCropland change. framework This is uncertainty critical as local framework governments, is critical aid agencies,as local governments, and the private aid sector agen- reactcies, toand shifting the private land sector cover react patterns to shifting by, for land example, cover buildingpatterns by, new for infrastructure example, building and enactingnew infrastructure natural resource and enacting protections. natural resource protections. Our Our findings findings show show thatthat land cover in in the the Region Region is is already already dynamic; dynamic; substantial substantial hu- human-causedman-caused land land cover cover change change has has already already occurred inin everyevery ecoregion.ecoregion. The The changes changes we we documentdocument highlight highlight new new needs needs for for infrastructure infrastructure and and suggest suggest hotspots hotspots for for future future change. change. WeWe also also demonstrate demonstrate that that large large shifts shifts are are occurring occurring outside outside of of forests. forests. Greenhouse Greenhouse gas gas monitoringmonitoring focusing focusing onlyonly on deforestation deforestation will will omit omit emissions emissions from from the the conversion conversion of sys- of systemstems like like wooded wooded grasslands. grasslands. The The uncertainty uncertainty information providedprovided byby ourour statistical statistical frameworkframework suggests suggests the the degree degree to to which which these these observations observations apply apply at at the the level level of of particular particular domains,domains, such such as as individual individual countries countries or or ecosystems. ecosystems.

5.5. Conclusions Conclusion EffectiveEffective land land cover cover monitoring monitoring is is necessary necessary to to address address the the environmental environmental and and social social problemsproblems facing facing ourour planet. Here Here we we used used op openen access access data data and and tools tools in conjunction in conjunction with withlocal local environmental environmental knowledge knowledge to monitor to monitor land land use dynamics use dynamics across across thirty thirty years years in East- in Easternern Africa. Africa. The The results results show show clear clear patterns patterns in land in land use useintensification intensiﬁcation in the in theregion: region: The Thedevelopment development of ofcropland cropland is isexpanding expanding into into previously previously intact intact savannas andand woodlands,woodlands, threateningthreatening critical critical ecosystems ecosystems known known for for biodiversity biodiversity and and resulting resulting in in net net reductions reductions in in woodywoody vegetation. vegetation. Land 2021, 10, 150 14 of 15

Author Contributions: Conceptualization, S.P.H., Z.Y., and W.B.C.; methodology S.P.H., Z.Y., and W.B.C.; software, Z.Y., W.B.C., N.G., and E.L.B.; validation, E.L.B. and Z.Y.; formal analysis, E.L.B. and Z.Y.; investigation, P.O., N.G., Z.Y., S.O., E.O., W.B.C., S.P.H., and E.L.B.; resources, Z.Y. and N.G.; data curation, Z.Y. and E.L.B.; Writing—Original draft preparation, E.L.B.; Writing—Review and editing, S.P.H., Z.Y., and P.O.; visualization, E.L.B.; supervision, S.P.H.; project administration, S.P.H. funding acquisition, S.P.H.; All authors have read and agreed to the published version of the manuscript. Funding: This research and APC were funded by NASA under SERVIR Applied Science Team grant NNH16AD021. Data Availability Statement: The code and data used in this investigation are available on GitHub: https://github.com/bullocke/eastafrica. Acknowledgments: We are grateful for the support provided by the SERVIR Science Coordina- tion Office and the US Agency for International Development, and for the technical cooperation supported by Sylvia Wilson and the SilvaCarbon Program. The authors are also indebted to the RCMRD/SERVIR-Eastern and Southern Africa Project. Conflicts of Interest: The authors declare no conflict of interest.

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