Evaluating the potential of ALS data to increase the efficiency of aboveground biomass estimates in tropical peat–swamp forests

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha −1 with a standard error of 11.17 Mgha −1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha −1 with a slightly smaller standard error of 10.57 Mgha −1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha −1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precisionWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia DOG n 52, 17 March 2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Eduardo González-Ferreiro at different institutionsS

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

Oil palm (Elaeis guineensis) production in Indonesia: carbon footprint and diversification options

Oil palm (Elaeis guineensis) is a uniquely valuable palm as source of low-cost vegetable oil. However, the success and method of its expansion (monoculture plantation) especially in biodiversity-rich Indonesia and Malaysia have made it one of the most controversial crops of the world. One of the policy consequences of the boycotts and debate is the Renewable Energy Directive (RED) of European countries that sets binding targets for the emission savings to be achieved when oils are used as feedstock of biofuel. Exporting countries such as Indonesia need to have reliable data on the carbon footprint of their product across production systems and the products’ lifecycle. Diversification of oil palm plantations starts to gain attention as a strategy to increase farmer resilience. The objectives of this thesis were (1) to estimate the carbon footprint of palm oil production in Indonesia when it is used as biofuel and express it as CO2 equivalent and emissions saving, and (2) to explore mixed oil palm systems as diversification strategy to increase farmer benefit and to reduce the carbon footprint. Through a survey and sample collection in more than 20 plantations distributed over Sumatra, Kalimantan and Sulawesi we analysed the palm oil life cycle. Using the Biofuel Emission Reduction Estimator Scheme (BERES) emissions savings were differentiated by carbon debt (land use change) and current practices. Process-based modelling using WaNuLCAS (Water, Nutrient and Light Capture in Agroforestry System) helped explore intercropping systems beyond current practice. Results show that it is possible to achieve the high emission savings target with palm oil to comply with the RED requirement. Of companies with ‘good agricultural practice’ 40% and 25% of production can meet the 35% (2015) and 60% (2018) emissions savings standards, respectively. The larger the areas that were converted from high-C stock forest, the larger the fraction of peat, the larger the emissions from fertilizers, transportation and processing (incl. methane) and the lower the yield of Fresh Fruit Bunches (FFB), in a mix of production situations that is accounted for jointly (as is the case for ‘company’ level assessments), the harder it is to achieve emission savings. While fertilizer application increases FFB yield, it also increases N2O emissions. Selected mixed oil palm systems can provide considerable economic and environmental system improvements. The Land Equivalent Ratio of mixed oil palm – cacao systems can be 1.4, showing a superior way to achieve land sparing as a goal of efficient use of land, relative to monocultures for each commodity separately. Diversification should be a valid counterpart of current intensification research and policies to help make palm oil more sustainable from both social and environmental perspectives.</p

Tropical Peatland Hydrology Simulated With a Global Land Surface Model

Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM(Trop,Nat)) and drained (PEATCLSM(Trop,Drain)) tropical peatlands. Simulations with PEATCLSM(Trop,Nat) were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM(North,Nat)) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM(Trop,Drain) was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM(Trop,Drain) outperformed CLSM, PEATCLSM(North,Nat), and PEATCLSM(Trop,Nat) over drained sites. Despite the overall improvements of PEATCLSM(Trop,Nat) over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model.Peer reviewe

A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation

A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestatio

Practical Guide to Measuring Wetland Carbon Pools and Fluxes

Wetlands cover a small portion of the world, but have disproportionate infuence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fuxes. However, the underlying biogeochemical processes that afect wetland C pools and fuxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fuxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fuxes. We frst defne each of the major C pools and fuxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of fndings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions

Biomass forest modelling using UAV LiDAR data under fire effect

Mestrado em Engenharia Florestal e dos Recursos Naturais / Instituto Superior de Agronomia. Universidade de LisboaThe main goal of the study is to analyse the possibility of quantifying the loss of biomass in burned forest stands using Light Detection and Ranging (LiDAR) data. Since wildfires are not uncommon in Mediterranean areas, it is useful to quantify the magnitude of fire damage in forests. With the use of remote sensing, it is possible to plan post-fire recovery management and to quantify the losses of biomass and carbon stock. Mata Nacional de Leiria (MNL) was chosen, because, after the fire in October 2017, it showed areas with low and medium-high fire severity. MNL is divided in several rectangular management units (MU). To achieve our objective, it was necessary to find a MU with burned and unburned areas. In this selection process, we used Sentinel-2 images. The fire severity was estimated by deriving a spectral index related with the effects of fire and to compute the temporal difference (pre- minus post-fire) of this index, the delta normalized burn ratio (DNBR). Forest inventory was carried out in four plots installed in the selected MU. Allometric equations were used to estimate values of stand aboveground biomass. These values were used to fit a relationship with data extracted from LiDAR cloud metrics. The LiDAR data were acquired with a VLP-16 Velodyne LiDAR PUCK™ mounted on an Unmanned Aerial Vehicles (UAV) at an altitude of 60 m above the ground. The point clouds were then processed with the FUSION software until a cloud metrics was generated and then regression models were used to fit equations related to LiDAR-derived parameters. Two biomass equations were fit, one with the whole tree metrics having a R² = 0,95 and a second one only considering the tree crown metrics presenting a R² = 0,93. The state of the forest (unburned/burned) was significant on the final equationN/