Prieto-Amparán Jesús A, Villarreal-Guerrero Federico, Martínez-Salvador Martin, Manjarrez-Domínguez Carlos, Vázquez-Quintero Griselda, Pinedo-Alvarez Alfredo
Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Chihuahua, Chihuahua, Mexico.
Facultad de Ciencias Agrotecnológicas, Universidad Autónoma de Chihuahua, Chihuahua, Chihuahua, Mexico.
PeerJ. 2019 Mar 21;7:e6617. doi: 10.7717/peerj.6617. eCollection 2019.
The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.
尽管墨西哥温带森林对维持生物多样性的重要性已得到广泛认可，但近几十年来其仍在持续减少。本研究利用陆地卫星传感器的卫星图像，分析土地利用/土地覆盖变化情景。图像对应的年份为1990年、2005年和2017年。这些情景应用于温带森林，旨在更好地了解土地利用/土地覆盖变化的模式。支持向量机（SVM）多光谱分类技术用于确定土地利用/土地覆盖类型，并通过卡帕指数进行验证。为了模拟土地利用/土地覆盖动态，使用了在Dinamica-EGO中开发的一个模型，该模型使用马尔可夫链、细胞自动机和证据权重的随机模型。对于本研究，提出了一个稳定情景、一个乐观情景和一个悲观情景。基于这三种情景的预测模拟到了2050年。识别并评估了五种土地利用/土地覆盖类型。它们是原始森林、次生森林、人类住区、无植被区域和水体。土地利用/土地覆盖变化分析结果显示次生森林面积大幅增加。原始森林的表面积从1990年的55.8%减少到2017年的37.7%。此外，三种预测情景估计原始森林的表面积将进一步减少，尤其是在稳定情景和悲观情景下。这凸显了采取保护措施以保护这些生态系统及其服务的重要性，而且可能需要紧急实施。基于所获得的准确性和生成的模型，这些方法的结果可作为一种决策工具，为区域自然资源的可持续管理做出贡献。
