Almeida Bruna, Cabral Pedro, Fonseca Catarina, Gil Artur, Scemama Pierre
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal; Centre for Ecology, Evolution and Environmental Changes (cE3c); Azorean Biodiversity Group (GBA), University of the Azores (UAc), Faculty of Sciences and Technology (FCT-UAc); Rua Mãe de Deus, Campus Universitário de Ponta Delgada, Edifício do Complexo Científico, 9500-321 Ponta Delgada, Portugal.
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal.
Sci Total Environ. 2024 Dec 20;957:177527. doi: 10.1016/j.scitotenv.2024.177527. Epub 2024 Nov 20.
Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.
在全球范围内，土地利用变化导致地上生物量（AGB）的损失始终大于增加。森林破碎化是生物多样性丧失和自然资本消耗的主要驱动因素。测量景观特征并分析森林景观格局的变化对于评估森林生态系统对经济和人类福祉的贡献至关重要。本研究使用细胞自动机（CA）系统预测2036年和2054年的国家森林分布，并通过斑块、类别和景观层面的景观指标评估生态系统状况。我们计算了130个指标，并应用方差阈值法去除方差较低的特征，测试了不同的阈值。通过主成分分析结合特征重要性技术对首先筛选出的指标进行进一步分析，以选择并排名前10个指标：有效网格大小、分割指数、平均回转半径、最大斑块指数、平均核心面积、核心面积百分比、辛普森均匀度指数、互信息、辛普森多样性指数和平均邻接指数。第十一个选定的指标是AGB密度，它是生态系统状况的一种结构测量指标，也是森林碳储存和封存评估的替代指标。从2000年到2018年，国家AGB森林碳储量从131.5兆吨降至91.3兆吨，2036年和2054年的预期值分别为71.8兆吨和55.3兆吨。景观测量定量描述了森林动态，为了解景观演变的结构、配置和变化提供了见解。本研究强调了CA模型绘制大规模森林资源和预测未来发展情景的能力，为保护和环境管理决策提供了有用信息。此外，它还通过评估森林范围及其状况指标提供了支持生态系统核算的测量方法。
