scholarly journals Land Use Increases the Correlation between Tree Cover and Biomass Carbon Stocks in the Global Tropics

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1217
Author(s):  
Manan Bhan ◽  
Simone Gingrich ◽  
Sarah Matej ◽  
Steffen Fritz ◽  
Karl-Heinz Erb

Tree cover (TC) and biomass carbon stocks (CS) are key parameters for characterizing vegetation and are indispensable for assessing the role of terrestrial ecosystems in the global climate system. Land use, through land cover change and land management, affects both parameters. In this study, we quantify the empirical relationship between TC and CS and demonstrate the impacts of land use by combining spatially explicit estimates of TC and CS in actual and potential vegetation (i.e., in the hypothetical absence of land use) across the global tropics (~23.4° N to 23.4° S). We find that land use strongly alters both TC and CS, with stronger effects on CS than on TC across tropical biomes, especially in tropical moist forests. In comparison to the TC-CS correlation observed in the potential vegetation (biome-level R based on tropical ecozones = 0.56–0.90), land use strongly increases this correlation (biome-level R based on tropical ecozones = 0.87–0.94) in the actual vegetation. Increased correlations are not only the effects of land cover change. We additionally identify land management impacts in closed forests, which cause CS reductions. Our large-scale assessment of the TC-CS relationship can inform upcoming remote sensing efforts to map ecosystem structure in high spatio-temporal detail and highlights the need for an explicit focus on land management impacts in the tropics.

2005 ◽  
Vol 9 (7) ◽  
pp. 1-31 ◽  
Author(s):  
Gregory P. Asner ◽  
David E. Knapp ◽  
Amanda N. Cooper ◽  
Mercedes M. C. Bustamante ◽  
Lydia P. Olander

Abstract The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil.


2016 ◽  
Vol 9 (9) ◽  
pp. 2973-2998 ◽  
Author(s):  
David M. Lawrence ◽  
George C. Hurtt ◽  
Almut Arneth ◽  
Victor Brovkin ◽  
Kate V. Calvin ◽  
...  

Abstract. Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the following questions. (1) What are the effects of LULCC on climate and biogeochemical cycling (past–future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy, and are there regional land-management strategies with the promise to help mitigate climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from LULCC relative to all forcings, separation of biogeochemical from biogeophysical effects of land use, the unique impacts of land-cover change vs. land-management change, modulation of land-use impact on climate by land–atmosphere coupling strength, and the extent to which impacts of enhanced CO2 concentrations on plant photosynthesis are modulated by past and future land use.LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use data set, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to LULCC. In this paper, we describe LUMIP activity (2), i.e., the LUMIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to simulations requested in the CMIP6 DECK and historical simulations and other CMIP6 MIPs including ScenarioMIP, C4MIP, LS3MIP, and DAMIP. LUMIP includes a two-phase experimental design. Phase one features idealized coupled and land-only model simulations designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential land-cover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the potential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request for selected variables (reporting model output data separately for primary and secondary land, crops, pasture, and urban land-use types). It is essential that modeling groups participating in LUMIP adhere to the experimental design as closely as possible and clearly report how the model experiments were executed.


Anthropocene ◽  
2019 ◽  
Vol 28 ◽  
pp. 100228 ◽  
Author(s):  
Colin J. Courtney Mustaphi ◽  
Claudia Capitani ◽  
Oliver Boles ◽  
Rebecca Kariuki ◽  
Rebecca Newman ◽  
...  

2016 ◽  
Author(s):  
David M. Lawrence ◽  
George C. Hurtt ◽  
Almut Arneth ◽  
Victor Brovkin ◽  
Kate V. Calvin ◽  
...  

Abstract. Human land-use activities have resulted in large to the Earth surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the questions: (1) What are the effects of LULCC on climate and biogeochemical cycling (past–future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy and are there regional land-management strategies with promise to help mitigate and/or adapt to climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from land-use change relative to fossil fuel emissions, separation of biogeochemical from biogeophysical effects of land-use, the unique impacts of land-cover change versus land management change, modulation of land-use impact on climate by land-atmosphere coupling strength, and the extent that impacts of enhanced CO2 concentrations on plant photosynthesis are modulated by past and future land use. LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use dataset, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to-. In this paper, we describe the LUMIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to experiments from the CMIP core, ScenarioMIP, and C4MIP. LUMIP includes a two-phase experimental design. Phase one features idealized coupled and land-only model experiments designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential land-cover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the potential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request (primary and secondary land, crops, pasture, urban). It is essential that modeling groups participating in LUMIP adhere to the experimental design as closely as possible.


Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 571
Author(s):  
Mathew Mpanda ◽  
Almas Kashindye ◽  
Ermias Aynekulu ◽  
Elvis Jonas ◽  
Todd S. Rosenstock ◽  
...  

Forests and woodlands remain under threat in tropical Africa due to excessive exploitation and inadequate management interventions, and the isolated success stories of tree retention and tree cover transition on African agricultural land are less well documented. In this study, we characterize the status of tree cover in a landscape that contains forest patches, fallows, and farms in the southern part of Uluguru Mountains. We aimed to unveil the practices of traditional tree fallow system which is socially acceptable in local settings and how it provides a buffering effects to minimize forest disturbances and thus represents an important step towards tree cover transition. We assessed land cover dynamics for the period of 1995 to 2020 and compared tree stocking for forest patches, fallows, and farms. We found that tree biomass carbon stocks were 56 ± 5 t/ha in forest patches, 33 ± 7 t/ha in fallows, and 9 ± 2 t/ha on farms. In terms of land cover, farms shrank at intensifying rates over time for the entire assessment period of 1995–2020. Forest cover decreased from 1995–2014, with the reduction rate slowing from 2007–2014 and the trend reversing from 2014–2020, such that forest cover showed a net increase across the entire study period. Fallow consistently and progressively increased from 1995–2020. We conclude that traditional tree fallows in the study site remain a significant element of land management practice among communities, and there appears to be a trend towards intensified tree-based farming. The gains in fallowed land represent an embracing of a traditional land management system that supports rotational and alternate uses of cropping space as well as providing a buffering effect to limit over-exploitation of forests. In order to maximize tree cover and carbon stocks in the farm landscape, this well-known traditional tree fallow system can be further optimized through the incorporation of additional innovations.


2011 ◽  
Vol 13 (5) ◽  
pp. 695-700
Author(s):  
Zhihua TANG ◽  
Xianlong ZHU ◽  
Cheng LI

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