Deforestation since independence: a quantitative assessment of four decades of land-cover change in Malawi

Land cover has changed rapidly across the tropics over the past century; however, detailed historical information describing the extent and possible drivers of such change is widely lacking. Here, we constructed a history of land-cover change at the district level in Malawi over a 37-year period from 1972 to 2009, the immediate post-colonial phase. Overall, there was a loss of 12 760 km2 (36%) of original forested area but also 11 161 km2 of new forest establishment, resulting in a relatively modest overall net loss of 1 599 km2 (5%). We correlated changes in deforestation and forest establishment with changes in socio-economic variables derived from spatially explicit data from the same time period. Deforestation was positively correlated with (in order of influence) changes in male school attendance, sex ratio, population density, hospital bed numbers, protected areas and dependency rate, but negatively correlated with changes in cattle density; forest establishment broadly showed the inverse relationships with the same variables. Although direct drivers of deforestation are well known for Malawi and much of Africa, the significance of socio-economic variables within this study can help to understand the underlying social pressures behind such drivers. In particular, development, population pressure and demographic factors are important predictors of deforestation rate within our study area.

过去一个世纪以来，热带地区的土地覆被（land cover）变化迅猛，但目前仍普遍缺乏能够详细阐述此类变化规模及其潜在驱动因素的历史资料。本研究构建了1972年至2009年这37年间马拉维（Malawi）县域尺度的土地覆被变化历史序列，该时段正值该国后殖民初期阶段。整体而言，研究区内原有森林面积减少了12760平方千米（占比36%），同时新增造林（forest establishment）面积达11161平方千米，最终仅出现1599平方千米（5%）的小幅净森林损失。本研究将森林砍伐（deforestation）与造林的变化，与同期空间显式数据（spatially explicit data）所衍生的社会经济变量变化进行了相关性分析。森林砍伐与以下社会经济变量变化呈正相关（按影响程度排序）：男性入学率、性别比、人口密度、医院床位数、保护区覆盖情况以及抚养比，而与牲畜密度变化呈负相关；造林活动则与上述变量大体呈现相反的相关关系。尽管马拉维乃至非洲大部分地区的森林砍伐直接驱动因素已较为明确，但本研究中社会经济变量的显著性，有助于进一步解析此类驱动因素背后潜藏的社会压力。尤其值得注意的是，发展水平、人口压力与人口统计因素是本研究区域内森林砍伐率的重要预测因子。