Rosa Maria Roman-Cuesta

Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD

Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.

ENVIRONMENTAL AND HUMAN FACTORS INFLUENCING FIRE TRENDS IN ENSO AND NON-ENSO YEARS IN TROPICAL MEXICO

Tropical and subtropical areas present the vast majority of contemporary global fires. Despite the human origin of most of these fires, little is known of how environmental and socioeconomic variables contribute to the spatial patterns of fire incidence and burned areas. The tropical Mexican State of Chiapas represents a good case study to analyze these interactions, due to the availability of official data, and its similarities to other tropical countries, in terms of environmental and socioeconomic characteristics. This study evaluates the relative importance of human-related and environmental variables in determining the distribution of the number of fires and area burned in the tropical State of Chiapas in years of normal and extreme climatic conditions (non-El Nino vs. El Nino). We have searched for causal relationships among fire, environmental, and socioeconomic variables in Chiapas using path analysis. Results of this study show a major importance of environmental variables in non-El Nino years, s...

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high‐quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini‐reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth‐atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub‐disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large‐scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

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.

How can climate policy benefit from comprehensive land‐use approaches?

Schemes that reward developing countries for mitigating greenhouse-gas (GHG) emissions through forest preservation and restoration are becoming more common. However, efforts to reduce GHG emissions must also consider food production. This creates an apparent conflict, given that agricultural production – a key driver of GHG emissions as a consequence of forest clearance – will increase as human populations continue to grow. We propose that a mosaic of small patches of forest mixed with cropland enables sustainable intensification of agriculture by minimizing soil degradation. Economic analyses of this mixed land-use concept suggest an improvement of long-term economic performance of 19–25% relative to conventional industrial agriculture with large-scale monocropping. Adopting this approach requires farm management plans, landscape zoning, and new instruments to finance sustainable agriculture. We conclude that climate policy and food production can be reconciled through an integrative landscape concept th...

Synchronous fire activity in the tropical high Andes: an indication of regional climate forcing

Global climate models suggest enhanced warming of the tropical mid and upper troposphere, with larger temperature rise rates at higher elevations. Changes in fire activity are amongst the most significant ecological consequences of rising temperatures and changing hydrological properties in mountainous ecosystems, and there is a global evidence of increased fire activity with elevation. Whilst fire research has become popular in the tropical lowlands, much less is known of the tropical high Andean region (>2000 masl, from Colombia to Bolivia). This study examines fire trends in the high Andes for three ecosystems, the Puna, the Paramo and the Yungas, for the period 1982-2006. We pose three questions: (i) is there an increased fire response with elevation? (ii) does the El Niño- Southern Oscillation control fire activity in this region? (iii) are the observed fire trends human driven (e.g., human practices and their effects on fuel build-up) or climate driven? We did not find evidence of increased fire activity with elevation but, instead, a quasicyclic and synchronous fire response in Ecuador, Peru and Bolivia, suggesting the influence of high-frequency climate forcing on fire responses on a subcontinental scale, in the high Andes. ENSO variability did not show a significant relation to fire activity for these three countries, partly because ENSO variability did not significantly relate to precipitation extremes, although it strongly did to temperature extremes. Whilst ENSO did not individually lead the observed regional fire trends, our results suggest a climate influence on fire activity, mainly through a sawtooth pattern of precipitation (increased rainfall before fire-peak seasons (t-1) followed by drought spells and unusual low temperatures (t0), which is particularly common where fire is carried by low fuel loads (e.g., grasslands and fine fuel). This climatic sawtooth appeared as the main driver of fire trends, above local human influences and fuel build-up cyclicity.

A quantitative comparison of methods for classifying burned areas with LISS¿III imagery

Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite‐based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north‐east Spain) by means of an IRS LISS‐III image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non‐pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost‐effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved.