Deforestation since independence: a quantitative assessment of four decades of land-cover change in Malawi
Deforestation since independence: a quantitative assessment of four decades of land-cover change in Malawi
复制标题
DOI：
10.2989/20702620.2016.1233777
复制
发表时间：
2017-01-01
影响因子：
0.8
通讯作者：
Willcock, Simon
中科院分区：
文献类型：
--
作者：
Bone, Rachel A.;Parks, Kate E.;Willcock, Simon
在过去的世纪里，整个热带地区的土地覆盖发生了迅速的变化;然而，描述这种变化的程度和可能的驱动因素的详细历史信息却普遍缺乏。在这里，我们构建了马拉维地区一级土地覆盖变化的历史，从1972年到2009年的37年期间，即后殖民阶段。总体而言，原有森林面积减少了12 760 km（2）（36%），但新增森林面积也减少了11 161 km（2），总体净损失相对较小，为1 599 km（2）（5%）。我们将森林砍伐和森林建立的变化与来自同一时期空间明确数据的社会经济变量的变化相关联。砍伐森林与男性入学率、性别比、人口密度、医院床位数、保护区和抚养率的变化呈正相关（按影响顺序），但与牛密度的变化呈负相关;森林建立与相同的变量大致呈反比关系。尽管马拉维和非洲大部分地区的森林砍伐的直接驱动因素众所周知，但本研究中社会经济变量的重要性有助于了解这些驱动因素背后的潜在社会压力。特别是，发展，人口压力和人口因素是我们研究区域内森林砍伐率的重要预测因素。
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 km(2) (36%) of original forested area but also 11 161 km(2) of new forest establishment, resulting in a relatively modest overall net loss of 1 599 km(2) (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.