Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio

Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data

Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied area-based approaches to predict rot occurrence, rot severity, and rot volume , at an area level. Ground reference data were collected from harvester operations in 2019–2021. Predictor variables were calculated from multi-temporal remotely sensed data together with environmental variables. Response variables from the harvester data and predictor variables from remotely sensed data were aggregated to grid cells and to forest stands. Random Forest models were built for the different combinations of response variables and predictor subsets, and validated with both random- and spatial cross-validation. The results showed that it was not possible to estimate rot occurrence and rot severity with the applied modeling procedure (pR2: 0.00–0.16), without spatially close training data. The better performance of rot volume models (pR2: 0.12–0.37) was mainly due to the correlation between timber volume and rot volum

Chemoenzymatic Late-Stage Modifications Enable Downstream Click-Mediated Fluorescent Tagging of Peptides

Aromatic prenyltransferases from cyanobactin biosynthetic pathways catalyse the chemoselective and regioselective intramolecular transfer of prenyl/geranyl groups from isoprene donors to an electron-rich position in these macrocyclic and linear peptides. These enzymes often demonstrate relaxed substrate specificity and are considered useful biocatalysts for structural diversification of peptides. Herein, we assess the isoprene donor specificity of the N1-tryptophan prenyltransferase AcyF from the anacyclamide A8P pathway using a library of 22 synthetic alkyl pyrophosphate analogues, of which many display reactive groups that are amenable to additional functionalization. We further used AcyF to introduce a reactive moiety into a tryptophan-containing cyclic peptide and subsequently used click chemistry to fluorescently label the enzymatically modified peptide. This chemoenzymatic strategy allows late-stage modification of peptides and is useful for many applications

Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data

Tropical forests are a key component of the global carbon cycle, and mapping their carbon density is essential for understanding human influences on climate and for ecosystem-service-based payments for forest protection. Discrete-return airborne laser scanning (ALS) is increasingly recognised as a high-quality technology for mapping tropical forest carbon, because it generates 3D point clouds of forest structure from which aboveground carbon density (ACD) can be estimated. Area-based models are state of the art when it comes to estimating ACD from ALS data, but discard tree-level information contained within the ALS point cloud. This paper compares area-based and tree-centric models for estimating ACD in lowland old-growth forests in Sabah, Malaysia. These forests are challenging to map because of their immense height. We compare the performance of (a) an area-based model developed by Asner and Mascaro (2014), and used primarily in the neotropics hitherto, with (b) a tree-centric approach that uses a new algorithm ($\textit{itcSegment}$) to locate trees within the ALS canopy height model, measures their heights and crown widths, and calculates biomass from these dimensions. We find that Asner and Mascaro's model needed regional calibration, reflecting the distinctive structure of Southeast Asian forests. We also discover that forest basal area is closely related to canopy gap fraction measured by ALS, and use this finding to refine Asner and Mascaro's model. Finally, we show that our tree-centric approach is less accurate at estimating ACD than the best-performing area-based model (RMSE 18% vs 13%). Tree-centric modelling is appealing because it is based on summing the biomass of individual trees, but until algorithms can detect understory trees reliably and estimate biomass from crown dimensions precisely, areas-based modelling will remain the method of choice.This project was supported by a grant through the Human Modified Tropical Forests programme of NERC (NE/K016377/1). We thank members of the NERC Airborne Remote Sensing Facility and NERC Data Analysis Node for collecting and processing the data (project code MA14-14). David Coomes was supported by an International Academic Fellowship from the Leverhulme Trust. Lindsay Banin contributed field allometry data which were collected during her PhD at University Leeds, supported by NERC and a RGS Henrietta Hutton Grant. Oliver Phillips, Simon Lewis and Lan Qie provided census data collected as part of an ERC Advanced Grant (T-Forces)

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Nonvolant mammal megadiversity and conservation issues in a threatened central amazonian hotspot in Brazil

Amazonia National Park is located in southwestern Pará State in central Amazonia. The 10,707 km2 park is one of the largest protected areas in Brazil and is covered with pristine forests, but the region is threatened by dam construction projects. An incomplete mammal biodiversity inventory was conducted in the area during the late 1970s. Here, we present results of sampling from 7,295 live-trap nights, 6,000 pitfall-trap nights, more than 1,200 km of walking transect censuses, and approximately 3,500 camera-trap days, all conducted between 2012 and 2014. These sampling efforts generated a list of 86 known species of nonvolant mammals, making the park the single most species-rich area for nonvolant mammals both in the Amazon Basin and in the Neotropics as a whole. Amazonia National Park is a megadiverse site, as is indicated by its mammalian richness, which includes 15 threatened mammal species and 5 to 12 new species of small mammals. As such, it merits being a high-conservation priority and should be an important focus of Brazilian authorities’ and the international scientific community’s conservation efforts. A comprehensive conservation plan is urgently needed, especially given the ecological threats posed by dam construction. © The Author(s) 2016