Environmental Statistics Unit, Italian National Institute of Statistics, Rome, Italy.
Environ Manage. 2009 Nov;44(5):888-98. doi: 10.1007/s00267-009-9378-5. Epub 2009 Sep 29.
Although several studies have assessed Land Degradation (LD) states in the Mediterranean basin through the use of composite indices, relatively few have evaluated the impact of specific LD drivers at the local scale. In this work, a computational strategy is introduced to define homogeneous areas at risk and the main factors acting as determinants of LD. The procedure consists of three steps and is applied to a set of ten environmental indicators available at the municipality scale in Latium, central Italy. A principal component analysis extracting latent patterns and simplifying data complexity was carried out on the original data matrix. Subsequently, a k-means cluster analysis was applied on a restricted number of meaningful, latent factors extracted by PCA in order to produce a classification of the study area into homogeneous regions. Finally, a stepwise discriminant analysis was performed to determine which indicators contributed the most to the definition of homogeneous regions. Three classes of "risky" regions were identified according to the main drivers of LD acting at the local scale. These include: (i) soil sealing (coupled with landscape fragmentation, fire risk, and related processes), (ii) soil salinization due to agricultural intensification, and (iii) soil erosion due to farmland depopulation and land abandonment in sloping areas. Areas at risk for LD covered 56 and 63% of the investigated areas in 1970 and 2000, respectively.
虽然有几项研究通过使用综合指数评估了地中海盆地的土地退化(LD)状况,但很少有研究在当地尺度上评估特定 LD 驱动因素的影响。在这项工作中,引入了一种计算策略来定义处于危险中的同质区域和作为 LD 决定因素的主要因素。该程序由三个步骤组成,并应用于意大利中部拉齐奥地区市一级提供的十项环境指标集。对原始数据矩阵进行了主成分分析,以提取潜在模式并简化数据复杂性。随后,在 PCA 提取的有意义的潜在因子的数量有限的情况下应用了 k-均值聚类分析,以便对研究区域进行同质区域分类。最后,进行了逐步判别分析,以确定哪些指标对同质区域的定义贡献最大。根据当地尺度上 LD 的主要驱动因素,确定了三类“风险”区域。这些包括:(i)土壤封盖(与景观破碎化、火灾风险和相关过程相关),(ii)农业集约化导致的土壤盐渍化,以及(iii)由于农田人口减少和陡坡土地弃耕导致的土壤侵蚀。1970 年和 2000 年,LD 风险区域分别覆盖了研究区域的 56%和 63%。