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

Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods

In this paper four different delineation methods based on airborne laser scanning (ALS) and hyperspectral data are compared over a forest area in the Italian Alps. The comparison was carried out in terms of detected trees, while the ALS based methods are compared also in terms of attributes estimated (e.g. height). From the experimental results emerged that ALS methods outperformed hyperspectral one in terms of tree detection rate in two of three cases. The best results were achieved with a method based on region growing on an ALS image, and by one based on clustering of raw ALS point cloud. Regarding the estimates of the tree attributes all the ALS methods provided good results with very high accuracies when considering only big trees

Soil properties zoning of agricultural fields based on a climate-driven spatial clustering of remote sensing time series data

The identification of zones within an agricultural field that respond differently to environmental factors and agronomic management is a key requirement for the adoption of more precise and sustainable agricultural practices. Several approaches based on spatial clustering methods applied to different data sources, e.g. yield maps, proximal sensors and soil surveys, have been proposed in the last decades. The current availability of a huge amount of free remote sensing data allows to apply these approaches to agricultural areas where ground or proximal data are not available. However, in order to provide useful agronomic management information, it is essential that the zoning obtained by clustering is linked to the underlying spatial variability of soil properties. In this work we explore the hypothesis that the response of crop vigor to temporal climate variability, assessed by remote sensing data time series, selected to correspond to specific growth phases and seasonal climate patterns, provides indications on the variability of soil properties within agricultural fields, for both herbaceous and tree crops. NDVI time-series for 38 years (1984–2021) were obtained for fourteen non-irrigated herbaceous and tree crop fields in Central Italy, from multispectral satellites data (Landsat 5/7/8, Sentinel 2). The Standardized Precipitation-Evapotranspiration Index (SPEI) was used to classify time series into three climatic classes (dry/normal/wet) for five different periods of the growth season, covering the main phenological phases. K-means clustering was used to identify patterns of crop growth from climatically classified image sets, as well as for all the bulked images for comparison (bulk clustering). Clustering results were compared with soil maps obtained from spatialized ground data, for soil texture (clay, silt and sand), soil organic matter and available soil water (ASW). The agreement between the different clustering results and soil maps was assessed by the Adjusted Rand Index. Agreement with soil maps varied depending on the field, the phenological phase considered and the soil property considered. Climate driven clustering from long, late growth season periods best matched soil properties, both for herbaceous and tree crops, despite being based on a limited number of images. The clustering from images spanning a longer growth period for dry years systematically outpaced the bulk clustering for silt, sand and ASW, while the clustering for normal climatic conditions was the best for organic matter. The performance of the matching between clustering and soil maps increased with soil variability significantly more (P < 0.05) than in the bulk clustering (mean slopes respectively 0.468 ± 0.167; 0.113 ± 0.270). The integration of the SPEI climatic index into the clustering procedure systematically improved the identification of zones with homogeneous soil properties, highlighting that a greater attention should be posed to the climate-crop-field interactions when using remotely sensed images

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind. </jats:sec

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

Individual Tree Species Classification from Airborne Multisensor Imagery Using Robust PCA

Remote sensing of individual tree species has many applications in resource management, biodiversity assessment, and conservation. Airborne remote sensing using light detection and ranging (LiDAR) and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise coalignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study, we developed a generic workflow to map tree species at ITC level from hyperspectral imagery and LiDAR data using a combination of well established and recently developed techniques. The workflow uses a nonparametric image registration approach to coalign images, a multiclass normalized graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel- and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust principal component analysis to prune the hyperspectral dataset and reveal subtle difference among species.Department for Environment, Food and Rural AffairsThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JSTARS.2016.256940

Global airborne laser scanning data providers database (GlobALS)-A new tool for monitoring ecosystems and biodiversity

Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.</jats:p

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