REDD+: Quick Assessment of Deforestation Risk Based on Available Data

Abstract

Abstract:The evaluation of the future dynamics of deforestation is essential to creating the basis for the effective implementation of REDD+ (Reducing Emissions from Deforestation and forest Degradation) initiatives. Such evaluation is often a challenging task, especially for countries that have to cope with a critical lack of data and capacities, higher uncertainties, and competing interests. We present a new modeling approach that makes use of available and easily accessible data sources to predict the spatial location of future deforestation. This approach is based on the Random Forest algorithm, which is a machine learning technique that enables evidence-based, data-driven decisions and is therefore often used in decision-making processes. Our objective is to provide a straightforward modeling approach that, without requiring cost-intensive assessments, can be applied in the early stages of REDD+, for a stepwise implementation approach of REDD+ projects in regions with limited availability of data, capital, technical infrastructure, or human capacities. The presented model focuses on building business-as-usual scenarios to identify and rank potentially suitable areas for REDD+ interventions. For validation purposes we applied the model to data from Nicaragua.

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

“…There is a high demand for improved measurement methods for use in such programs as but not limited to Reducing Emissions from Deforestation and Forest Degradation (REDD+; [2]), forests as carbon sinks for national CO 2 reduction plan [3] and for meeting the challenges of modeling future climate scenarios by integrating terrestrial biogeochemical feedback [4]. By collecting accurate forest data, we can better estimate and interpret forest dimensions to evaluate various forest ecosystem services, such as biodiversity [5], carbon stocks and CO 2 uptake [6,7], and aboveground biomass (AGB; [8]).…”

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

“…One of the important functions a forest performs is the sequestration of CO 2 through photosynthetic processes, and some global actions tend to rely on this sequestration to mitigate the impacts of global warming [3]. Reducing Emissions from Deforestation and Forest Degradation (REDD+) [4], the global goals discussed in the Paris Agreement [5], and the Sustainable Development Goals (SDGs) set by the United Nations [6] are important frameworks and goals that promote such forest functions. To commit to strategic actions and to shed light on the achievement of these goals, advances are required in the realm of precision forestry.…”

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

“…Some areas show transitions away from green areas, which 2 of 18 results in substantial impacts on local to global ecosystems, sociocultural and economic impacts [7,8]. To mitigate these impacts, known cooperative agencies and organizations are planning actions for the recovery of green areas [9,10]. Consistent monitoring of the rapid environmental changes in green vegetative coverages is important for conservation and maintaining the sustainability of the natural environment.…”