Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

Carly Voight,Christina Garcia,Karla Hernandez-Aguilar,Said Gutierrez

doi:10.3390/rs11070823

Abstract

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.

Highlights

Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain
This study employed a multi-layer perceptron (MLP) neural network model to determine the sites most susceptible to future deforestation based on the anthropogenic conversion of forest in the Maya Golden Landscape (MGL) using remote sensing and spatial variables that represent the regional drivers of deforestation
This study employed a multi-layer perceptron (MLP) neural network model to predict the sites most susceptible to future deforestation based on recent forest cover change in the Maya Golden

Summary

Introduction

Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain. About half of the world’s tropical forests have been cleared [1], and between 1980 and 2000, over 80% of new agricultural land originated from forests [2] This deforestation has unknown long-term effects on biodiversity, rare species, ecosystem processes and functions, climate patterns, and the existence of important resources such as medicines and crop relatives [3,4]. The region includes the primary biological corridor in southern Belize, which is the only remaining broadleaf forest link between the Maya Mountains and Remthotee Sloenws.l2a0n1d9,b1r1o, xadFlOeRafPfEoErResRtsEVthIEaWt extend to the coast. This connection is critically important on bo3thofa 17 national and regional scale as part of the Mesoamerican biological corridor. The MGL is part of typMiceaslolyamfienreicsac’aslSeealvnadMsiatye‐as,pwehciifcihc i[s3t3h].eTsehciosnmdeltahrogedstcoreumldaibneinagptprolipeidcaml roarienfworiedsetliynttohecoAnmseerrvicaatsio, n plaanftneirntgheinAmotahzeorn.laTnhdesScaelpveas,Measypaescuiapllpyorrtesgoivoenrs4w00itbhirdsesvpeerceiefsi,naabnocuiat loncoenbsiltlriaoinntosv, earswiantwerainygto primoriigtrizaetotrhyebpirrdotinecdtiivoinduoafltsh, raenadtecnrietdicaalrpeaosp.ulations of threatened species such as the jaguar, Yucatán black howler monkey, and Baird’s tapir [6]

Methods

Findings

Discussion

Conclusion