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Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha-1 and R² : 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha-1 and R² of 0.81 for the combination of optical and SAR data in the GBM model. [ABSTRACT FROM AUTHOR]

Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30 m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1 m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC = 0.893, real-world AUC = 0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. After ground-truthing, we expect these shrubland maps and models will have many research and stewardship applications including wildlife conservation, invasive species mitigation and natural climate solutions. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas. [ABSTRACT FROM AUTHOR]

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Large-footprint waveform light detection and ranging (lidar) data have been widely used in above-ground forest biomass estimation. Waveform metrics derived from basic statistics (e.g. percentile of energy) of the lidar waveform, such as canopy height and height of median energy, have been applied to biomass estimation in numerous studies. In this study, a set of metrics based on Gaussian decomposition (GD) results were developed and evaluated for forest above-ground biomass estimation using NASA's laser vegetation imaging sensor (LVIS) data. The GD metrics were designed to explicitly incorporate lidar intensity and vertical structures of canopy layers for biomass estimation. The proposed GD metrics used information related to the above-ground height of each Gaussian centroid and the Gaussian area index (GAI), where GAI is the area covered by a Gaussian function. Two types of novel GD metrics were developed: (1) percentileheight GAI metrics expressing the GAI summation of a subset of Gaussian centroids located within a certain percentile height range; and (2) height-weighted GAI metrics, a summation of GAIs of a waveform weighted by the corresponding heights of their Gaussian centroids. A biomass regression model was built by eight newly developed GD metrics using GAI information and five pre-existing GD-derived metrics that have not previously been used for biomass estimation. The performance of the regression model was then compared to another regression model using 12 previously published metrics (non-GD metrics). The Random Forests (RF) regression algorithm was employed for predicting biomass. The RF out-of-bag results indicated that above-ground biomass estimations using GD metrics achieved significantly better results than those derived from non-GD metrics for deciduous plots (19% lower root mean square error (RMSE), 25% higher coefficient of determination (R²), and marginally better results in coniferous plots (4% lower RSME, 6% higher R²). The combination of GD and non-GD metrics achieved comparable biomass estimation results to the model using exclusively GD metrics. GD metrics also showed strong correlation with forest attributes such as mean diameter at breast height (DBH) and stem density. This study contributes to the usage of GD results for accurate estimation of forest above-ground biomass in large-footprint lidar waveform data in temperate deciduous forests, because temperate deciduous forests have been proved challenging in regard to lidar-derived biomass estimations. [ABSTRACT FROM AUTHOR]