Martin Herold

An assessment of deforestation and forest degradation drivers in developing countries

Countries are encouraged to identify drivers of deforestation and forest degradation in the development of national strategies and action plans for REDDC. In this letter we provide an assessment of proximate drivers of deforestation and forest degradation by synthesizing empirical data reported by countries as part of their REDDC readiness activities, CIFOR country profiles, UNFCCC national communications and scientific literature. Based on deforestation rate and remaining forest cover 100 (sub)tropical non-Annex I countries were grouped into four forest transition phases. Driver data of 46 countries were summarized for each phase and by continent, and were used as a proxy to estimate drivers for the countries with missing data. The deforestation drivers are similar in Africa and Asia, while degradation drivers are more similar in Latin America and Asia. Commercial agriculture is the most important driver of deforestation, followed by subsistence agriculture. Timber extraction and logging drives most of the degradation, followed by fuelwood collection and charcoal production, uncontrolled fire and livestock grazing. The results reflect the most up to date and comprehensive overview of current national-level data availability on drivers, which is expected to improve over time within the frame of the UNFCCC REDDC process.

Nondestructive estimates of above‐ground biomass using terrestrial laser scanning

Summary: Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68-0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57-29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps.s

How countries link REDD+ interventions to drivers in their readiness plans: implications for monitoring systems

Countries participating in the REDD+ scheme are in the readiness phase, designing policy interventions to address drivers of deforestation and forest degradation (DD). In order for REDD+ interventions to be effective, it is essential that they take into account the specific drivers that they aim to address. Moreover it is crucial to design systems that monitor the effectiveness of the planned interventions. In this article we provide a comprehensive and comparative assessment of interventions proposed by 43 REDD+ countries in 98 readiness documents. We summarize the types of interventions and assess if they are formulated referring to the drivers of DD that they are aiming to address. Based on this assessment we consider the implications for systems for monitoring effectiveness of proposed interventions. Most countries reviewed link proposed interventions to specific drivers of DD. The majority of the countries making this link have better driver data quality, in particularly those that present their data in ratio or ordinal terms. Proposed interventions focus not only on activities to reduce deforestation, but also on other forest related REDD+ activities such as sustainable forest management, which reduce forest degradation and enhance forest stocks. Moreover, driver-specific interventions often relate to drivers not only inside but also outside the forest sector. Hence we suggest that monitoring systems need to assess not only deforestation rates through remote sensing, but also degradation and other carbon stock changes within the forest, using more detailed ground level surveys and measurements. In addition, the performance of interventions outside the forest need to be monitored, even if the impacts of these cannot be linked to specific changes in forest carbon stock in specific locations.

Linking community-based and national REDD+ monitoring: a review of the potential

Countries participating in REDD+ schemes are required to establish a national monitoring system that keeps track of forest carbon changes over time. Community-based monitoring (CBM) can be useful for tracking locally driven forest change activities and their impacts. In this paper, we review some of the key issues regarding CBM and options to link CBM and national forest monitoring systems. More specifically, we highlight the importance of local drivers of deforestation and degradation and, thus, the relevance of community involvement in REDD+ implementation and monitoring; we review the scientific literature to better define the role and technical conditions under which CBM can contribute to national level monitoring; we develop a conceptual framework for linking local and national monitoring; and we analyze and synthesize 28 REDD+ country approaches to CBM. Finally, we provide recommendations for integrating CBM data into national monitoring systems.

REDD+ readiness: early insights on monitoring, reporting and verification systems of project developers

A functional measuring, monitoring, reporting and verification (MRV) system is essential to assess the additionality and impact on forest carbon in REDD+ (reducing emissions from deforestation and degradation) projects. This study assesses the MRV capacity and readiness of project developers at 20 REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam, using a questionnaire survey and field visits. Nineteen performance criteria with 76 indicators were formulated in three categories, and capacity was measured with respect to each category. Of the 20 projects, 11 were found to have very high or high overall MRV capacity and readiness. At the regional level, capacity and readiness tended to be highest in the projects in Brazil and Peru and somewhat lower in Cameroon, Tanzania, Indonesia and Vietnam. Although the MRV capacities of half the projects are high, there are capacity deficiencies in other projects that are a source of concern. These are not only due to limitations in technical expertise, but can also be attributed to the slowness of international REDD+ policy formulation and the unclear path of development of the forest carbon market. Based on the study results, priorities for MRV development and increased investment in readiness are proposed.

Mobile devices for community-based REDD+ monitoring: a case study for Central Vietnam.

Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery.

Land management: data availability and process understanding for global change studies

In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling.

An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor

Abstract Wetlands are important providers of ecosystem services and key regulators of climate change. They positively contribute to global warming through their greenhouse gas emissions, and negatively through the accumulation of organic material in histosols, particularly in peatlands. Our understanding of wetlands’ services is currently constrained by limited knowledge on their distribution, extent, volume, interannual flood variability and disturbance levels. We present an expert system approach to estimate wetland and peatland areas, depths and volumes, which relies on three biophysical indices related to wetland and peat formation: (1) long‐term water supply exceeding atmospheric water demand; (2) annually or seasonally water‐logged soils; and (3) a geomorphological position where water is supplied and retained. Tropical and subtropical wetlands estimates reach 4.7 million km2 (Mkm2). In line with current understanding, the American continent is the major contributor (45%), and Brazil, with its Amazonian interfluvial region, contains the largest tropical wetland area (800,720 km2). Our model suggests, however, unprecedented extents and volumes of peatland in the tropics (1.7 Mkm2 and 7,268 (6,076–7,368) km3), which more than threefold current estimates. Unlike current understanding, our estimates suggest that South America and not Asia contributes the most to tropical peatland area and volume (ca. 44% for both) partly related to some yet unaccounted extended deep deposits but mainly to extended but shallow peat in the Amazon Basin. Brazil leads the peatland area and volume contribution. Asia hosts 38% of both tropical peat area and volume with Indonesia as the main regional contributor and still the holder of the deepest and most extended peat areas in the tropics. Africa hosts more peat than previously reported but climatic and topographic contexts leave it as the least peat‐forming continent. Our results suggest large biases in our current understanding of the distribution, area and volumes of tropical peat and their continental contributions.

Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Connecting Earth observation to high-throughput biodiversity data

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.