Meroni Michele, Schucknecht Anne, Fasbender Dominique, Rembold Felix, Fava Francesco, Mauclaire Margaux, Goffner Deborah, Di Lucchio Luisa M, Leonardi Ugo
European Commission, Joint Research Centre, Directorate D - Sustainable Resources, Food Security Unit, Via Fermi 2749, 21027 Ispra, VA, Italy.
International Livestock Research Institute, P.O. Box 30709, 00100 Nairobi, Kenya.
Int J Appl Earth Obs Geoinf. 2017 Jul;59:42-52. doi: 10.1016/j.jag.2017.02.016.
Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.
为应对土地退化而开展的恢复性干预措施在干旱和半干旱地区实施，以改善植被覆盖和土地生产力。由于各种限制因素（如难以进入的地区、缺乏长期记录）以及缺乏标准化且经济实惠的方法，随着时间推移评估干预措施的成效具有挑战性。我们提出一种半自动方法，该方法利用遥感数据，就恢复性干预措施对植被覆盖的生物物理影响提供快速、标准化且客观的评估。归一化植被指数（NDVI）被用作植被覆盖的替代指标。认识到植被覆盖的变化自然是由季节性和年际气候变化等环境因素导致的，仅关注干预区域无法得出关于干预措施成功与否的结论。因此，我们采用一种比较方法，该方法分析干预区域的NDVI在干预前后相对于从一组与干预区域相似的候选区域中自动随机选取的多个对照点的时间变化。相似性是根据干预前图像的ISODATA分类得出的类别组成来定义的。该方法提供了干预区域与对照区域之间绿色度变化差异的大小和显著性估计。作为案例研究，该方法应用于在塞内加尔开展的15项恢复性干预措施。使用250米分辨率的MODIS数据和30米分辨率的Landsat数据对干预措施的影响进行分析。结果表明，仅在三分之一的分析干预措施中可检测到植被覆盖有显著改善，这与基于实地观察和高分辨率图像视觉分析的独立定性评估结果一致。农村发展机构可能会潜在地使用所提出的方法对恢复性干预措施进行初步筛选。
