Data from: Mapping tropical dry forest succession using multiple criteria spectral mixture analysis

Tropical dry forests (TDFs) in the Americas are considered the first frontier of economic development with less than 1% of their total original coverage under protection. Accordingly, accurate estimates of their spatial extent, fragmentation, and degree of regeneration are critical in evaluating the success of current conservation policies. This study focused on a well-protected secondary TDF in Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Guanacaste, Costa Rica. We used spectral signature analysis of TDF ecosystem succession (early, intermediate, and late successional stages), and its intrinsic variability, to propose a new multiple criteria spectral mixture analysis (MCSMA) method on the shortwave infrared (SWIR) of HyMap image. Unlike most existing iterative mixture analysis (IMA) techniques, MCSMA tries to extract and make use of representative endmembers with spectral and spatial information. MCSMA then considers three criteria that influence the comparative importance of different endmember combinations (endmember models): root mean square error (RMSE); spatial distance (SD); and fraction consistency (FC), to create an evaluation framework to select a best-fit model. The spectral analysis demonstrated that TDFs have a high spectral variability as a result of biomass variability. By adopting two search strategies, the unmixing results showed that our new MCSMA approach had a better performance in root mean square error (early: 0.160/0.159; intermediate: 0.322/0.321; and late: 0.239/0.235); mean absolute error (early: 0.132/0.128; intermediate: 0.254/0.251; and late: 0.191/0.188); and systematic error (early: 0.045/0.055; intermediate: −0.211/−0.214; and late: 0.161/0.160), compared to the multiple endmember spectral mixture analysis (MESMA). This study highlights the importance of SWIR in differentiating successional stages in TDFs. The proposed MCSMA provides a more flexible and generalized means for the best-fit model determination than common IMA methods.美洲的热带干旱森林（Tropical dry forests, TDFs）被视作经济开发的前沿区域，其原始总面积中仅有不足1%得到保护。故而，精准估算其空间分布范围、破碎化程度与恢复状况，对评估当前保护政策的实施成效至关重要。本研究聚焦哥斯达黎加瓜纳卡斯特圣罗莎国家公园（Santa Rosa National Park, SRNP）环境监测超级站点内一处保护状况良好的次生热带干旱森林。研究团队针对热带干旱森林生态系统演替（早期、中期与晚期演替阶段）的光谱特征及其内在变异性开展分析，并基于HyMap影像的短波红外（shortwave infrared, SWIR）波段，提出一种全新的多准则光谱混合分析（multiple criteria spectral mixture analysis, MCSMA）方法。与多数现有迭代混合分析（iterative mixture analysis, IMA）技术不同，MCSMA尝试提取并利用兼具光谱与空间信息的代表性端元。随后，该方法综合三项影响不同端元组合（端元模型）相对重要性的准则——均方根误差（root mean square error, RMSE）、空间距离（spatial distance, SD）与分量一致性（fraction consistency, FC），构建评估框架以筛选最优拟合模型。光谱分析结果表明，热带干旱森林因生物量变异存在显著的光谱变异性。通过采用两种搜索策略，混合像元分解结果显示，相较于多端元光谱混合分析（multiple endmember spectral mixture analysis, MESMA），全新的MCSMA方法在多项指标上均表现更优：均方根误差（早期：0.160/0.159；中期：0.322/0.321；晚期：0.239/0.235）、平均绝对误差（mean absolute error，早期：0.132/0.128；中期：0.254/0.251；晚期：0.191/0.188）以及系统误差（systematic error，早期：0.045/0.055；中期：−0.211/−0.214；晚期：0.161/0.160）。本研究凸显了短波红外波段在区分热带干旱森林演替阶段中的关键作用。所提出的MCSMA方法相较于常见的迭代混合分析方法，为最优拟合模型的确定提供了更为灵活且通用的技术途径。