Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Forest Aboveground Biomass Estimation Using Multi-Source Remote Sensing Data in Temperate Forests

Forests are a crucial part of global ecosystems. Accurately estimating aboveground biomass (AGB) is important in many applications including monitoring carbon stocks, investigating forest degradation, and designing sustainable forest management strategies. Remote sensing techniques have proved to be a cost-effective way to estimate forest AGB with timely and repeated observations. This dissertation investigated the use of multiple remotely sensed datasets for forest AGB estimation in temperate forests. We compared the performance of Landsat and lidar data—individually and fused—for estimating AGB using multiple regression models (MLR), Random Forest (RF) and Geographically Weight Regression (GWR). Our approach showed MLR performed similarly to GWR and both were better than RF. Integration of lidar and Landsat inputs outperformed either data source alone. However, although lidar provides valuable three-dimensional forest structure information, acquiring comprehensive lidar coverage is often cost prohibitive. Thus we developed a lidar sampling framework to support AGB estimation from Landsat images. We compared two sampling strategies—systematic and classification-based—and found that the systematic sampling selection method was highly dependent on site conditions and had higher model variability. The classification-based lidar sampling strategy was easy to apply and provides a framework that is readily transferable to new study sites. The performance of Sentinel-2 and Landsat 8 data for quantifying AGB in a temperate forest using RF regression was also tested. We modeled AGB using three datasets: Sentinel-2, Landsat 8, and a pseudo dataset that retained the spatial resolution of Sentinel-2 but only the spectral bands that matched those on Landsat 8. We found that while RF model parameters impact model outcomes, it is more important to focus attention on variable selection. Our results showed that the incorporation of red-edge information increased AGB estimation accuracy by approximately 6%. The additional spatial resolution improved accuracy by approximately 3%. The variable importance ranks in the RF regression model showed that in addition to the red- edge bands, the shortwave infrared bands were important either individually (in the Sentinel-2 model) or in band indices. With the growing availability of remote sensing datasets, developing tools to appropriately and efficiently apply remote sensing data is increasingly important

Spaceborne Lidar for Estimating Forest Biophysical Parameters

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover

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

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping