Population, Land Use and Deforestation in the Pan Amazon Basin: a Comparison of Brazil, Bolivia, Colombia, Ecuador, Perú and Venezuela

This paper discusses the linkages between population change, land use, and deforestation in the Amazon regions of Brazil, Bolivia, Colombia, Ecuador, Perú, and Venezuela. We begin with a brief discussion of theories of population–environment linkages, and then focus on the case of deforestation in the PanAmazon. The core of the paper reviews available data on deforestation, population growth, migration and land use in order to see how well land cover change reflects demographic and agricultural change. The data indicate that population dynamics and net migration exhibit to deforestation in some states of the basin but not others. We then discuss other explanatory factors for deforestation, and find a close correspondence between land use and deforestation, which suggests that land use is loosely tied to demographic dynamics and mediates the influence of population on deforestation. We also consider national political economic contexts of Amazon change in the six countries, and find contrasting contexts, which also helps to explain the limited demographic-deforestation correspondence. The paper closes by noting general conclusions based on the data, topics in need of further research and recent policy proposals.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42720/1/10668_2003_Article_6977.pd

Spatial and temporal patterns of biotic exchanges of CO{sub 2} between the atmosphere and tropical landscapes and their role in the global carbon cycle. Final report

Land cover change is one of the most important components of global change. It might be argued that over the next 20 to 50 years the global effects on ecosystems and human habitability from land cover conversion will be much larger than any estimated to arise from climate change. Only a few datasets exist, and these suffer from a number of technical and interpretive problems. There are three areas of uncertainty which need much better documentation and analysis: (1) the state (ie information on biomass, net primary production, etc.) and distribution on existing land cover, (2) the rate and distribution of land cover conversion, both historically and currently, and (3) the underlying driving forces which determine the rate and extent of land cover conversion. Some combination of historical reconstruction and remote sensing are needed to refine the first two areas. Future research can be developed around three general areas: (a) acquisition and analysis of new data on existing and pre-disturbance land cover, using a combination of satellite data, historical reconstructions, and maps, (b) acquisition and analysis of mapped data on the distribution of primary agents of land cover change, such as deforestation or agricultural expansion using satellite data for the contemporary period (since 1978), and tabular census data for historical periods, (c) development of models, both explanatory and numerical, which are abased on simple extrapolation for short term analyses and socio-economic factors for long term analyses

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions

Integrating JERS-1 imaging radar and elevation models for mapping tropical rainforest communities in far North Queensland, Australia

The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics

Modelling and monitoring land-cover change processes in tropical regions

Transformations in terrestrial ecosystems are increasingly regarded as an important element of global change. Quantitative data on where, when and why land-cover changes take place globally are still incomplete. This article reviews recent approaches to the monitoring and modelling of deforestation and dryland degradation in tropical regions. The review highlights the requirement to tailor the investigation method to the specific research question of interest. Different techniques to monitor land-cover changes at regional scales are analysed. The following modelling scenarios are discussed and illustrated by specific studies: projection of future land-cover changes with descriptive models, explanation of land-cover changes with empirical models, projection of future spatial patterns of changes with spatial statistical models, test of scenarios on future changes in land-cover with dynamic ecosystem models, and design of policy interventions with economic models. The article stresses the needs for a better integration of social science knowledge in land-cover change models and for a comprehensive theory of land-use changes

Técnicas avançadas de sensoriamento remoto aplicadas ao estudo de mudanças climáticas e ao funcionamento dos ecossistemas amazônicos Advanced remote sensing techniques for global changes and Amazon ecosystem functioning studies

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.<br>This paper aims to assess the contribution of remote sensing technology in addressing key questions raised by the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The answers to these questions foster the knowledge on the climatic, biogechemical and hydrologic functioning of the Amazon, as well as on the impact of human activities at regional and global scales. Remote sensing methods allow integrating information on several processes at different temporal and spatial scales. By doing so, it is possible to perceive hidden relations among processes and structures, enhancing their teleconnections. Key advances in the remote sensing science are summarized in this article, which is particularly focused on information that would not be possible to be retrieved without the concurrence of this technology