Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR

Abstract. Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

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Detection of large above ground biomass variability in lowland forest ecosystems by airborne LiDAR

Biogeosciences Discussions ◽

10.5194/bgd-9-11815-2012 ◽

2012 ◽

Vol 9 (8) ◽

pp. 11815-11842 ◽

Cited By ~ 4

Author(s):

J. Jubanski ◽

U. Ballhorn ◽

K. Kronseder ◽

J. Franke ◽

F. Siegert

Keyword(s):

Climate Change ◽

Climate Models ◽

Forest Type ◽

Airborne Lidar ◽

Above Ground Biomass ◽

Inventory Data ◽

Intergovernmental Panel ◽

Lowland Forest ◽

Change Models ◽

Ground Biomass

Abstract. Quantification of tropical forest Above Ground Biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne Light Detection and Ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square meter of 2–4 resulted in the best cost-benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 46%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong GHG emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

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Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1421946112 ◽

2015 ◽

Vol 112 (30) ◽

pp. E4065-E4074 ◽

Cited By ~ 95

Author(s):

Rebecca G. Asch

Keyword(s):

Climate Change ◽

Climate Models ◽

Coastal Upwelling ◽

Southern Oscillation ◽

Larval Abundance ◽

Boundary Current ◽

Intergovernmental Panel ◽

Change Models ◽

Larval Fishes ◽

Decadal Climate

Climate change has prompted an earlier arrival of spring in numerous ecosystems. It is uncertain whether such changes are occurring in Eastern Boundary Current Upwelling ecosystems, because these regions are subject to natural decadal climate variability, and regional climate models predict seasonal delays in upwelling. To answer this question, the phenology of 43 species of larval fishes was investigated between 1951 and 2008 off southern California. Ordination of the fish community showed earlier phenological progression in more recent years. Thirty-nine percent of seasonal peaks in larval abundance occurred earlier in the year, whereas 18% were delayed. The species whose phenology became earlier were characterized by an offshore, pelagic distribution, whereas species with delayed phenology were more likely to reside in coastal, demersal habitats. Phenological changes were more closely associated with a trend toward earlier warming of surface waters rather than decadal climate cycles, such as the Pacific Decadal Oscillation and North Pacific Gyre Oscillation. Species with long-term advances and delays in phenology reacted similarly to warming at the interannual time scale as demonstrated by responses to the El Niño Southern Oscillation. The trend toward earlier spawning was correlated with changes in sea surface temperature (SST) and mesozooplankton displacement volume, but not coastal upwelling. SST and upwelling were correlated with delays in fish phenology. For species with 20th century advances in phenology, future projections indicate that current trends will continue unabated. The fate of species with delayed phenology is less clear due to differences between Intergovernmental Panel on Climate Change models in projected upwelling trends.

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Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei

Forests ◽

10.3390/f7110259 ◽

2016 ◽

Vol 7 (12) ◽

pp. 259 ◽

Cited By ~ 9

Author(s):

Eunji Kim ◽

Woo-Kyun Lee ◽

Mihae Yoon ◽

Jong-Yeol Lee ◽

Yowhan Son ◽

...

Keyword(s):

Rain Forest ◽

Tropical Rain Forest ◽

Airborne Lidar ◽

Lidar Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Airborne Lidar Data

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The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-00320.1 ◽

2018 ◽

Vol 99 (11) ◽

pp. 2341-2359 ◽

Cited By ~ 44

Author(s):

M. J. Roberts ◽

P. L. Vidale ◽

C. Senior ◽

H. T. Hewitt ◽

C. Bates ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Time Scales ◽

Climate Models ◽

Water Cycle ◽

Regional Scale ◽

Coupled Model ◽

Convective Precipitation ◽

Climate Simulation ◽

Intergovernmental Panel

AbstractThe time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).

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Assessment of Trace Gas Emissions From Wild Fires in Different Vegetation Types in Northern Ghana: Implications for Global Warming

Environment and Natural Resources Research ◽

10.5539/enrr.v5n2p37 ◽

2015 ◽

Vol 5 (2) ◽

pp. 37

Author(s):

Emmanuel Nyadzi ◽

Mathew I. S. Ezenwa ◽

Benjamin K. Nyarko ◽

A. A. Okhimamhe ◽

Thomas T. Bagamsah ◽

...

Keyword(s):

Climate Change ◽

Carbon Monoxide ◽

Global Warming ◽

Northern Ghana ◽

Vegetation Types ◽

Above Ground Biomass ◽

Biomass Density ◽

Savanna Woodland ◽

Ground Biomass ◽

Wild Fires

Biomass burning in Northern Ghana is a major cause for concern because of its potential contribution to global warming, hence climate change. This study assessed the emission of trace gases from human activities in the Guinea savanna of Northern Ghana using the guidelines of the Intergovernmental Panel on Climate Change. Carbon content of biomass was determined from four different vegetation covers in the study area; namely, widely open savanna woodland, grass/herb with scattered trees, open savanna woodland and closed savanna woodland. Under each vegetation cover, five plots (1 m x 1 m) were demarcated for the estimation of above-ground biomass density. Using the combustion furnace method, emitted carbon, methane and carbon monoxide were estimated. Results showed that the emitted methane (CH4) and carbon monoxide (CO) differed significantly (p<0.05) under all the vegetation types. The gases were in perfect correlation (r=1.00) with the quantity of above-ground biomass density and carbon released, with more CO being emitted. Emission of CH4 and CO per hectare of burnt area in the open savanna woodland category was the highest with 0.001719 ton and 0.045119 ton respectively. Over time, emission of these gases may increase their atmospheric concentration, causing major health problems. The contribution to global warming, thus climate change, may also become quite significant. This underscores the fact that existing flaws in the wild fire management policy of Ghana must be effectively dealt with and appropriately implemented with regular reviews to reduce the annual wild fires that are very rampant in Northern Ghana, especially during the dry season.

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Atmospheric Blocking and Mean Biases in Climate Models

Journal of Climate ◽

10.1175/2010jcli3728.1 ◽

2010 ◽

Vol 23 (23) ◽

pp. 6143-6152 ◽

Cited By ~ 134

Author(s):

Adam A. Scaife ◽

Tim Woollings ◽

Jeff Knight ◽

Gill Martin ◽

Tim Hinton

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Horizontal Resolution ◽

Small Scale ◽

Model Data ◽

Atmospheric Blocking ◽

Intergovernmental Panel ◽

Weather And Climate ◽

Main Culprit

Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

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An Overview of CMIP5 and the Experiment Design

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-11-00094.1 ◽

2012 ◽

Vol 93 (4) ◽

pp. 485-498 ◽

Cited By ~ 8089

Author(s):

Karl E. Taylor ◽

Ronald J. Stouffer ◽

Gerald A. Meehl

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Experiment Design ◽

Global Climate Models ◽

Model Output ◽

Earth System ◽

Intergovernmental Panel ◽

System Models ◽

Earth System Models

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

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