Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize
Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize
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DOI：
10.3390/rs11070823
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
C. Voight;Karla Hernandez-Aguilar;Christina Garcia;S. Gutierrez
中科院分区：
文献类型：
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作者：
C. Voight;Karla Hernandez-Aguilar;Christina Garcia;S. Gutierrez
在全世界，热带森林及其所包含的生物多样性正在以惊人的速度减少。虽然伯利兹南部被普遍认为是森林覆盖率很高的地区，但它正日益受到不可持续的农业做法的威胁。森林砍伐数据使森林管理人员能够有效地分配资源，并为适当的养护和管理决策提供信息。这项研究利用卫星图像来分析最近的森林覆盖和砍伐在南部伯利兹模型脆弱性，并确定最容易受到未来森林损失的地区。在谷歌地球引擎中进行了森林覆盖变化分析，使用Landsat 8图像的监督分类，以地面真实的土地覆盖点作为训练数据。建立了一个基于区域毁林驱动因素的多层感知器神经网络模型，用于预测森林损失的潜在空间格局和规模。该评估表明，农业前沿将继续扩大到最近未受影响的森林，预计成熟森林覆盖率将从2016年的75.0%下降到2026年的71.9%。这项研究是最新的森林覆盖评估和第一次脆弱性和预测评估在南部伯利兹与保护规划，监测和管理的直接应用。
Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management.