Assessment of drivers of forest changes using multi-temporal analysis and boosted regression trees model: a case study of Nyeri County, Central Region of Kenya

Abstract

The Central Region of Kenya has undergone significant changes in land cover due to a broad range of drivers. These changes are more pronounced in forestland conversions. Past researches within the study area have identified drivers of land cover change without quantifying the influence of these drivers. Predictor variables include population density, precipitation, elevation, slope, forest fires, soil texture, proximity to roads, rives and towns. Land cover changes were analyzed using multi-temporal land cover maps between year 1990 and 2014. Boosted regression trees model was applied to determine the significant drivers and quantify their relative influence on key forestland transitions. The local and spatial influence of the drivers has further been analyzed by geographical weighted regression using coefficients determined at each sample point. Significant land cover changes continuously occurred over the study period. Forestland reduced from 38.90% in 1990 to 38.14% in 2014. Grassland reduced from 32.59 to 22.57%, cropland increased from 28.05 to 38.83% and wetland changed from 0.07 to 0.04%. Other land which constitutes of bare land and built up increased from 0.38 to 0.42%. The results show population density had the highest contribution to forestland changes throughout the study period, with a minimum contribution of 20.02% to a maximum of 26.04%. Other significant variables over the study period are precipitation, slope, elevation and the proximity variables. The results indicate that the relative influence of the drivers to forestland conversion varies with time, location and type of transition.

Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya

“…For instance, Serneels and Lambin (2001) noted accessibility to markets and agroclimatic factors as critical drivers in the Narok district. In addition, Mwangi et al (2020) reported population, proximity to rivers, and proximity to roads as crucial drivers of cropland conversions in Central Kenya. Moreover, Were et al (2014) found soil pH, population density, precipitation, distance to towns, and rivers to be the significant drivers of LULCCs in the Eastern Mau forest reserve.…”

“…The challenges stem from an interplay of production factors such as land, income, market access, and prevailing climate conditions (Giller et al 2021). Different from developed regions, unsustainable land-use practices such as charcoal burning, illegal encroachments, overgrazing, and relaxed enforcement of the law encompass the prevalent drivers of forestland, grassland, and wetland losses (Baldyga et al 2008;Ewane 2021;Mwangi et al 2020;Nakalembe et al 2017). Consequently, these losses induce massive cropland conversions that have severe implications for ecosystem service provision (Song and Deng 2017), hydrological balances (Baldyga et al 2008), and food production (Hoque et al 2020).…”

“…For example, de Espindola et al ( 2021) combined satellite information and variables related to proximity, land management, technological resources, and environmental variables to assess drivers of LULCCs in the Amazon basin. Mwangi et al (2020) combined boosted regression trees and geographically weighted regression to determine the significance and model the spatial influence of the drivers of LULCCs in Central Kenya. Nevertheless, in Kenya, Were et al (2014) employed a logistic regression approach to uncover the drivers of LULCCs in Kenya-Afromontane forest environments.…”

Electronics, Close-Range Sensors and Artificial Intelligence in Forestry

“…As the relationship between forest loss and impact factors differs by country and region, studies are being conducted on various regions. Mwangi et al analyzed the relationship between forest loss and impact factors on randomly selected sites using land coverage maps in the Central Region and analyzed that topographic factors (altitude, slope), distance from roads and distance from rivers are the main causes of forest loss [117]. This study analyzed the influence of topographic factors and forest loss, unlike our research, which showed that the closer the distance from the road and the closer the river, the easier the transportation, resulting in forest loss.…”

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

“…Some studies that have been conducted in Kenya to estimate the above-ground biomass (AGB) stocks are not up to date, and the result is that detailed, complete, and rigorous assessments of AGB stocks are not easy to find [25,26]. These studies also show disconcerting planetary health challenges, such as a decline in forest cover and biodiversity loss across various regions [27][28][29][30][31][32], mainly due to increasing population pressure and demand for agricultural land that has led to deforestation [29,30,33,34]. The decreasing forest cover has decimated the amount of AGB and carbon stocks in the country [25,26,35].…”