Carbon sequestration, biodiversity and social structures in Southern Amazonia: models and implementation of carbon-optimized land management strategies


Climate change will increase precipitation variability – i. e. extreme events like droughts will occur more often also in the tropics and mean temperature will ultimately increase. Land use intensification is associated with (a) losses of ecosystem services like the loss of natural vegetation and associated ecosystem functions in the global and regional climate system, an (b) increasing releases of greenhouse gases (GHG), and (c) the reduction of livelihoods. This project aims at providing interdisciplinary solutions for these problems. Three regions along the land use frontier of Southern Amazonia were selected: Southern Pará: most active deforestation; Northern Mato Grosso: young soy bean production; Central Mato Grosso: established cultivation (>20 years) and adapted mechanized cropping (e.g. no till). Analyses focus on soil carbon (C) turnover, climate, ecosystem functions and socio-economic processes. Simulation models will be combined as software packages to support the decision-making process based on field and acquired data, including a step-by-step up-scaling from local to landscape and regional scale. All research and implementation activities include direct involvement of the stakeholders. Furthermore, joint field experiments for improving C storage and ecosystem functions will be performed in tight cooperation with an NGO founded by the farmers' organization of Mato Grosso. A combined computer-based decision support platform will be developed, including simulation models to run region-specific impacts of different scenarios of land use options and climate change on GHG and C cycling. This will be a highly valuable tool for regional planning authorities. From the scenario calculations simplified versions (e.g. emission factors) will be made available as an easy-to-use decision support system for individual stakeholders. Results will be communicated directly to stakeholders, by human capacity building, and by promoting financially feasible, C-optimized land use techniques throughout tropical areas with similar conditions.
The target of the subproject on “Landscape scale land cover analysis and geodata management” is to analyse landscape scale LULCC to support decision-making for an optimized land management. We develop and apply a landscape-wide analysis approach integrating remote sensing and spatial modelling techniques to gain knowledge on how to mitigate existing and prevent future land use conflicts:
(1) Development of remote sensing-based analysis schemes to derive land use at high resolution (e.g. Landsat data) with regional coverage over the last 25 years
(2) Adaptation of machine learning and time series algorithms to cope with large datasets
(3) Development and application of remote sensing based indicators for assessing landscape patterns and its links with carbon sequestration potential at landscape level
(4) Spatially explicit modelling on landscape level to identify drivers and hot spots of change at the landscape scale
(5) Spatially explicit scenario-building of land use types according to different regional to sub-continental storylines
(6) Development of a spatial data infrastructure for the whole CarBioCial project including data management and web-based technologies for distributed data access in Germany and Brazil

Principal Investigators
Hostert, Patrick Prof. Dr. (Details) (Geomatics)
Lakes, Tobia Prof. Dr. (Details) (Geoinformatics)

Participating external organizations

Duration of Project
Start date: 06/2011
End date: 08/2016

Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography, Geography

Research Areas
Geofernerkundung, Landnutzung und Landnutzungswandel

Publications
Gollnow, F., Göpel, J., deBarros Viana Hissa, L., Schaldach, R., & Lakes, T. (2017). Scenarios of land-use change in a deforestation corridor in the Brazilian Amazon: combining two scales of analysis. Regional Environmental Change, 1-17. doi:10.1007/s10113-017-1129-1
Gollnow, F., Hissa, L. d. B. V., Rufin, P., & Lakes, T. (2018). Property-level direct and indirect deforestation for soybean production in the Amazon region of Mato Grosso, Brazil. Land Use Policy, 78, 377-385. doi:https://doi.org/10.1016/j.landusepol.2018.07.010
Gollnow, F., & Lakes, T. (2014). Policy change, land use, and agriculture: The case of soy production and cattle ranching in Brazil, 2001–2012. Applied Geography, 55(0), 203-211. doi:http://dx.doi.org/10.1016/j.apgeog.2014.09.003
Hissa, L. d. B. V., Müller, H., Aguiar, A. P. D., Hostert, P., & Lakes, T. (2016). Historical carbon fluxes in the expanding deforestation frontier of Southern Brazilian Amazonia (1985–2012). Regional Environmental Change, 1-13. doi:10.1007/s10113-016-1076-2
Müller, H., Griffiths, P., & Hostert, P. (2016). Long-term deforestation dynamics in the Brazilian Amazon—Uncovering historic frontier development along the Cuiabá–Santarém highway. International Journal of Applied Earth Observation and Geoinformation, 44, 61-69. doi:http://dx.doi.org/10.1016/j.jag.2015.07.005
Müller, H., Rufin, P., Griffiths, P., Barros Siqueira, A. J., & Hostert, P. (2015). Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sensing of Environment, 156(0), 490-499. doi:http://dx.doi.org/10.1016/j.rse.2014.10.014
Müller, H., Rufin, P., Griffiths, P., de Barros Viana Hissa, L., & Hostert, P. (2016). Beyond deforestation: Differences in long-term regrowth dynamics across land use regimes in southern Amazonia. Remote Sensing of Environment, 186, 652-662. doi:http://dx.doi.org/10.1016/j.rse.2016.09.012
Pinheiro, T., Escada, M., Valeriano, D., Hostert, P., Gollnow, F., & Müller, H. (2016). Forest degradation associated with logging frontier expansion in the Amazon: the BR-163 region in southwestern Pará, Brazil. Earth Interactions(2016).
Rufin, P., Müller, H., Pflugmacher, D., & Hostert, P. (2015). Land use intensity trajectories on Amazonian pastures derived from Landsat time series. International Journal of Applied Earth Observation and Geoinformation, 41(0), 1-10. doi:http://dx.doi.org/10.1016/j.jag.2015.04.010

Last updated on 2020-01-06 at 17:26