Nadine T. Laporte

A first map of tropical Africa's above-ground biomass derived from satellite imagery

Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000‐2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha −1 for a range of biomass between 0 and 454 Mg ha −1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully captured the regional distribution of AGB. The results showed a strong positive correlation (R 2 = 0.90) between the GLAS height metrics and predicted AGB.

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods.

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.

Expansion of Industrial Logging in Central Africa

Industrial logging has become the most extensive land use in Central Africa, with more than 600,000 square kilometers (30%) of forest currently under concession. With use of a time series of satellite imagery for the period from 1976 to 2003, we measured 51,916 kilometers of new logging roads. The density of roads across the forested region was 0.03 kilometer per square kilometer, but areas of Gabon and Equatorial Guinea had values over 0.09 kilometer per square kilometer. A new frontier of logging expansion was identified within the Democratic Republic of Congo, which contains 63% of the remaining forest of the region. Tree felling and skid trails increased disturbance in selectively logged areas.

Comparing ape densities and habitats in northern Congo: surveys of sympatric gorillas and chimpanzees in the Odzala and Ndoki regions

The conservation status of western lowland gorillas and central chimpanzees in western equatorial Africa remains largely speculative because many remote areas have never been surveyed and the impact of emergent diseases in the region has not been well documented. In this study, we compared ape densities and habitats in the Lokoué study area in Odzala National Park and the Goualougo Triangle in Nouabalé‐Ndoki National Park in northern Republic of Congo. Both of these sites have long been considered strongholds for the conservation of chimpanzees and gorillas, but supposedly differ in vegetative composition and relative ape abundance. We compared habitats between these sites using conventional ground surveys and classified Landsat‐7 ETM+ satellite images. We present density estimates via both standing‐crop and marked‐nest methods for the first time for sympatric apes of the Congo Basin. The marked‐nest method was effective in depicting chimpanzee densities, but underestimated gorilla densities at both sites. Marked‐nest surveys also revealed a dramatic decline in the ape population of Lokoué which coincided with a local Ebola epidemic. Normal baseline fluctuations in ape nest encounter rates during the repeated passages of marked‐nest surveys were clearly distinguishable from a 80% decline in ape nest encounter rates at Lokoué. Our results showed that ape densities, habitat composition, and population dynamics differed between these populations in northern Congo. We emphasize the importance of intensifying monitoring efforts and further refinement of ape survey methods, as our results indicated that even the largest remaining ape populations in intact and protected forests are susceptible to sudden and dramatic declines. Am. J. Primatol. 70:439–451, 2008.

National-scale estimation of gross forest aboveground carbon loss: a case study of the Democratic Republic of the Congo

Recent advances in remote sensing enable the mapping and monitoring of carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where national forest inventories (NFI) are either non-existent or out of date. Here we demonstrate a method for estimating national-scale gross forest aboveground carbon (AGC) loss and associated uncertainties using remotely sensed-derived forest cover loss and biomass carbon density data. Lidar data were used as a surrogate for NFI plot measurements to estimate carbon stocks and AGC loss based on forest type and activity data derived using time-series multispectral imagery. Specifically,

Satellite-based primary forest degradation assessment in the Democratic Republic of the Congo, 2000–2010

Primary forest extent, loss and degradation within the Democratic Republic of the Congo (DRC) were quantified from 2000 to 2010 by combining directly mapped forest cover extent and loss data (CARPE) with indirectly mapped forest degradation data (intact forest landscapes, IFL). Landsat data were used to derive both map inputs, and data from the GLAS (Geoscience Laser Altimetry System) sensor were employed to validate the discrimination of primary intact and primary degraded forests. In the year 2000, primary humid tropical forests occupied 104?455?kha of the country, with 61% of these forests classified as intact. From 2000 to 2010, 1.02% of primary forest cover was lost due to clearing, and almost 2% of intact primary forests were degraded due to alteration and fragmentation. While primary forest clearing increased by a factor of two between 2000?2005 and 2005?2010, the degradation of intact forests slightly decreased. Fragmentation and selective logging were the leading causes of intact forest degradation, accounting for 91% of IFL area change. The 10?year forest degradation rate within designated logging permit areas was 3.8 times higher compared to other primary forest areas. Within protected areas the forest degradation rate was 3.7 times lower than in other primary forest areas. Forest degradation rates were high in the vicinity of major urban areas. Given the observed forest degradation rates, we infer that the degradation of intact forests could increase up to two-fold over the next decade.

Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+

This paper presents an overview of the state of measurement and monitoring capabilities for forests in the context of REDD+ needs, with a focus on what is currently possible, where improvements are needed, and what capabilities will be advanced in the near-term with new technologies already under development. We summarize the role of remote sensing (both satellite and aircraft) for observational monitoring of forests, including measuring changes in their current and past extent for setting baselines, their carbon stock density for estimating emissions in areas that are deforested or degraded, and their regrowth dynamics following disturbance. We emphasize the synergistic role of integrating field inventory measurements with remote sensing for best practices in monitoring, reporting and verification. We also address the potential of remote sensing for enforcing safeguards on conservation of natural forests and biodiversity. We argue that capabilities exist now to meet operational needs for REDD+ measurement, reporting, and verification and reference levels. For some other areas of importance for REDD+, such as safeguards for natural forests and biodiversity, monitoring capabilities are approaching operational in the near term. For all REDD+ needs, measurement capabilities will rapidly advance in the next few years as a result of new technology as well as advances in capacity building both within and outside of the tropical forest nations on which REDD+ is primarily focused.

The role of science in Reducing Emissions from Deforestation and Forest Degradation (REDD)

Emissions of carbon from tropical deforestation and degradation currently account for 12–15% of total anthropogenic carbon emissions each year, and Reducing Emissions from Deforestation and Forest Degradation (REDD; including REDD+) is poised to be the primary international mechanism with the potential to reduce these emissions. This article provides a brief summary of the scientific research that led to REDD, and that continues to help refine and resolve issues of effectiveness, efficiency and equitability for a REDD mechanism. However, REDD deals only with tropical forests and there are other regions, ecosystems and processes that govern the sources and sinks of carbon in terrestrial ecosystems. Ongoing research will reveal which of these other flows of carbon are most important, and which of them might present further opportunities to reduce emissions (or enhance sinks) through environmental policy mechanisms, as well as how they might do this.

Will Passive Protection Save Congo Forests

Central Africa’s tropical forests are among the world’s largest carbon reserves. Historically, they have experienced low rates of deforestation. Pressures to clear land are increasing due to development of infrastructure and livelihoods, foreign investment in agriculture, and shifting land use management, particularly in the Democratic Republic of Congo (DRC). The DRC contains the greatest area of intact African forests. These store approximately 22 billion tons of carbon in aboveground live biomass, yet only 10% are protected. Can the status quo of passive protection — forest management that is low or nonexistent — ensure the preservation of this forest and its carbon? We have developed the SimCongo model to simulate changes in land cover and land use based on theorized policy scenarios from 2010 to 2050. Three scenarios were examined: the first (Historical Trends) assumes passive forest protection; the next (Conservation) posits active protection of forests and activation of the national REDD+ action plan, and the last (Agricultural Development) assumes increased agricultural activities in forested land with concomitant increased deforestation. SimCongo is a cellular automata model based on Bayesian statistical methods tailored for the DRC, built with the Dinamica-EGO platform. The model is parameterized and validated with deforestation observations from the past and runs the scenarios from 2010 through 2050 with a yearly time step. We estimate the Historical Trends trajectory will result in average emissions of 139 million t CO2 year-1 by the 2040s, a 15% increase over current emissions. The Conservation scenario would result in 58% less clearing than Historical Trends and would conserve carbon-dense forest and woodland savanna areas. The Agricultural Development scenario leads to emissions of 212 million t CO2 year-1 by the 2040s. These scenarios are heuristic examples of policy’s influence on forest conservation and carbon storage. Our results suggest that 1) passive protection of the DRC’s forest and woodland savanna is insufficient to reduce deforestation; and 2): enactment of a REDD+ plan or similar conservation measure is needed to actively protect Congo forests, their unique ecology, and their important role in the global carbon cycle.

Towards an Operational Forest Monitoring System for Central Africa

Characterizing and mapping land cover and land use change in the rain forests of Central African is a complex process. This complexity is marked by the diversity of land use practices across six different countries (Cameroon, Central African Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon, and Republic of Congo), the lack of full and continuous cloud-free coverage by any single optical remote sensing instrument, and the limited institutional capacity to implement mapping and monitoring activities.