Forecasting deforestation and carbon emissions in tropical developing countries facing demographic expansion: a case study in Madagascar

Abstract

Anthropogenic deforestation in tropical countries is responsible for a significant part of global carbon dioxide emissions in the atmosphere. To plan efficient climate change mitigation programs (such as REDD+, Reducing Emissions from Deforestation and forest Degradation), reliable forecasts of deforestation and carbon dioxide emissions are necessary. Although population density has been recognized as a key factor in tropical deforestation, current methods of prediction do not allow the population explosion that is occurring in many tropical developing countries to be taken into account. Here, we propose an innovative approach using novel computational and statistical tools, including R/GRASS scripts and the new phcfM R package, to model the intensity and location of deforestation including the effect of population density. We used the model to forecast anthropogenic deforestation and carbon dioxide emissions in five large study areas in the humid and spiny-dry forests of Madagascar. Using our approach, we were able to demonstrate that the current rapid population growth in Madagascar (+3.39% per year) will significantly increase the intensity of deforestation by 2030 (up to +1.17% per year in densely populated areas). We estimated the carbon dioxide emissions associated with the loss of aboveground biomass to be of 2.24 and 0.26 tons per hectare and per year in the humid and spiny-dry forest, respectively. Our models showed better predictive ability than previous deforestation models (the figure of merit ranged from 10 to 23). We recommend this approach to reduce the uncertainty associated with deforestation forecasts. We also underline the risk of an increase in the speed of deforestation in the short term in tropical developing countries undergoing rapid population expansion.

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

“…This may be explained by, among others, the quality of variables and their resolution, the lack of spatial information for variables related with underlying causes, and the percentage of gaps and clouds that increases uncertainty of the covers. Despite the relatively low performance of the models, previous research shows the WoE estimates are robust to low-performing overall models [52][53][54]. Future comparisons with different methods such as the ROC (the receiver operating characteristic, an analysis widely applied to assess the performance of spatial models) may illuminate the regions of parameter space in which the models presented here underperform.…”

Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier

“…However, the accuracy of point sampling is closely linked to the quality of the sampling design (Steininger et al, 2009) and does not enable the production of deforestation maps of the entire forest area. Such maps are however required to target conservation efforts and to allow consistent spatial analysis of deforestation (Vieilledent, Grinand, & Vaudry, 2013).…”

Multiple-scale prediction of forest loss risk across Borneo

“…in developing countries it is important to understand the dynamics of land-use change and predict which areas are at highest risk of forest loss. Multiple studies have attempted to quantify and predict future deforestation, most notably in the Amazon basin (Soares-Filho et al 2006;Rosa et al 2013), and in other forests around the world (Rideout et al 2013;Vieilledent et al 2013); however, to date we know of no analyses that predict future forest loss across the full extent of Borneo.…”

“…While many recent studies of land-use change allude to spatial scales, they typically do so implicitly by including distance to various landscape features (roads, rivers, population center, etc.) as a metric (e.g., (Rideout et al 2013;Rosa et al 2013;Vieilledent et al 2013), which is not a true multi-scale analysis (sensu McGarigal et al 2016). To our knowledge there have been no multi-scale optimization efforts applied to landscape change modeling.…”

“…To our knowledge there have been no multi-scale optimization efforts applied to landscape change modeling. Additionally, while several analyses consider some element of landscape composition (e.g., Rosa et al 2013;Vieilledent et al 2013;Rideout et al 2013), we are aware of no studies that have considered both the composition and configuration of the multi-scale neighbourhood around a point as a predictor variable.…”