Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories ...across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5-12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The prevalence of black bear (Ursus americanus) bark stripping in commercial redwood (Sequoia sempervirens (D. Don) Endl.) timber stands has been increasing in recent years. This stripping is a ...threat to commercial timber production because of the deleterious effects on redwood tree fitness. This study sought to unveil a remote sensing method to detect these damaged trees early and map their spatial patterns. By developing a timely monitoring method, forest timber companies can manipulate their timber harvesting routines to adapt to the consequences of the problem. We explored the utility of high spatial resolution UAV-collected hyperspectral imagery as a means for early detection of individual trees stripped by black bears. A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. This spectral information was assessed using the Jeffries-Matusita (JM) distance to determine regions along the electromagnetic spectrum that are useful for discerning these three-health classes. While we were able to distinguish healthy trees from trees with old damage, we were unable to distinguish healthy trees from recently damaged trees due to the inherent characteristics of redwood tree growth and the subtle spectral changes within individual tree crowns for the time period assessed. The results, however, showed that with further assessment, a time window may be identified that informs damage before trees completely lose value.
Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and ...environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.
Despite occupying almost a fifth of the global terrestrial vegetation system, savanna ecosystems are relatively understudied in the Earth observation field. As a result, their contribution to global ...socioecological functions, such as carbon sequestration, habitat provision, watershed protection, biodiversity, and communal supply of timber and non-timber products, is inadequately accounted for. Since lidar remote sensing has been proved to estimate accurately the three-dimensional structural attributes of vegetation, the author found it insightful to synthesize the application of this technique in the savannas as one of the steps towards addressing this knowledge gap. The synthesis evaluated the progress of current studies that primarily use lidar data in the savannas and identified the associated opportunities and challenges. For each selected application, three main questions are asked: (1) what is typically needed from lidar remote sensing? (2) what have we achieved already? And (3) what is the current status? The last question was further split into two: (a) what is lacking, if any? (b) what challenges need to be addressed? This article concludes by looking into the potential future of lidar remote sensing in the savannas and some recommendations are put forward accordingly.
Although savanna ecosystems cover about 20% of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. As a ...result, these ecosystems are globally more important than generally appreciated in the earth observation and modeling communities. Remote sensing has been proposed as an efficient tool in assessing the physical structure of an ecosystem which in turn is closely related to its ecological functionality such as carbon storage. Studies using Light Detection and Ranging (lidar) have demonstrated the technology's ability to measure canopy height and the strong relationship between canopy height and structural attributes such as aboveground biomass, but most of this work has focused on closed canopy forests. This study explored the applicability of spaceborne lidar to estimate canopy height as a pre-requisite for aboveground biomass and carbon storage assessment in savannas. The research used a case study of the Oak Savannas of Santa Clara in California, USA. Discrete return airborne lidar data was used to extract height metrics in plots coincident with waveform data from the Ice Cloud and land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS). Detailed analysis of GLAS waveforms was followed by non-parametric regression modeling to estimate maximum canopy height and 80th and 90th percentile vegetation heights. Existing methods were adapted with the inclusion of NDVI (as a canopy cover proxy) and interaction terms to increase utility in savanna ecosystems. Our main findings were that merely adopting the methods derived for forests would not produce adequate results. Maximum canopy height was estimated with better accuracy compared to percentile height metrics. The inclusion of NDVI and interaction terms improved maximum canopy height modeling much more than it did for the 80th and 90th percentile height modeling. Taller stands on flat terrain had the best results while shorter stands on steep terrain had the worst. Our work has demonstrated the capability of waveform lidar to assess vegetation structural attributes in savannas. The challenge in canopy height modeling using this technique in such ecosystems not only is limited to terrain slope but also includes the interacting influence of low canopy cover and short height. As such, we need special models for savanna areas in an effort to do global assessments of terrestrial vegetation structure using lidar. For future studies we recommend a closer look at the non-significant influence of canopy cover on the percentile canopy height models especially its implication on the subsequent biomass modeling.
•A method is presented for estimating canopy height in savannas using lidar waveforms.•Savannas need different models from those developed for forests.•Canopy cover is important to capture canopy heterogeneity associated with savannas.•Interaction terms significantly improve modeling compared to single variables alone•Short height stands on steep slopes pose the main challenge in modeling
Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These ...measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots
Waveforms from the Ice, Cloud and land Elevation Satellite have successfully estimated footprint-level canopy height and aboveground biomass even in structurally complex savanna ecosystems. However, ...at the landscape level wall-to-wall maps are preferred since they are more easily integrated with other geospatial data products. We evaluated and compared the utility of inverse distance weighting, cokriging, regression kriging and image segmentation methods to create wall-to-wall maps from footprint-level estimates of biomass across a 13 600-Ha Oak savanna landscape in Santa Clara county, California. The four methods estimated biomass with between 39% (inverse distance weighting) and 66% (image segmentation) of variance explained and RMSE of 42% and 32% of the mean, respectively. When more waveforms were available across or along track to characterize patch biomass with the image segmentation method, 78% of variance in biomass was explained (RMSE = 21% of the mean). Overall, the mean biomass estimated by the four methods did not differ significantly but a visual inspection of the output maps showed marked differences in the ability of each model to mimic the primary variable's landscape-level trend. We conclude that transects of lidar data can be used to create wall-to-wall biomass maps in savannas but the methods require a higher sampling intensity and informative auxiliary data to reproduce the variability of the biomass across the landscape. We recommend that future satellite lidar missions increase the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary.
The next planned spaceborne lidar mission is the Ice, Cloud and land Elevation Satellite 2 (ICESat-2), which will use the Advanced Topographic Laser Altimeter System (ATLAS) sensor, a photon counting ...technique. To pre-validate the capability of this mission for studying three dimensional vegetation structure in savannas, we assessed the potential of the measurement approach to estimate canopy height in an oak savanna landscape. We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA’s Goddard Space Flight Center. ATLAS-like data was generated using the MATLAS simulator, which adjusts MABEL data’s detected number of signal and noise photons to that expected from the ATLAS instrument. Transects flown over the Tejon ranch conservancy in Kern County, California, USA were used for this work. For each transect we chose to use data from the near infrared channel that had the highest number of photons. We segmented each transect into 50m, 25m and 14m long blocks and aggregated the photons in each block into a histogram based on their elevation values. We then used an automated algorithm to identify cut off points where the cumulative density of photons from the highest elevation indicates the presence of the canopy top and likewise where such cumulative density from the lowest elevation indicates the mean terrain elevation. MABEL derived height metrics were moderately correlated to discrete return lidar (DRL) derived height metrics (r2 and RMSE values ranging from 0.60 to 0.73 and 2.9m to 4.4m respectively) but MATLAS simulation resulted in more modest correlations with DRL indices (r2 ranging from 0.5 to 0.64 and RMSE from 3.6m to 4.6m). Simulations also indicated that the expected number of signal photons from ATLAS will be substantially lower, a situation that reduces canopy height estimation precision especially in areas of low density vegetation cover. On the basis of the simulated data, there is reason to believe that the ability of ICESat-2 to estimate height in savannas will be comparable to the original ICESat mission although the respective sensors have different measurement principles.
Quantification of tree canopy area and aboveground biomass is essential for monitoring ecosystems' ecological functionalities, e.g., carbon sequestration and habitat provision. Miombo woodlands are ...vastly existent in developing countries that often lack resources to acquire LiDAR data or high spatiospectral resolution remote sensing data that have been proven to accurately estimate these structural attributes. This study explored the utility of freely available (via Google Maps) high (1-m) resolution red, green, and blue (RGB) satellite imagery in combination with object-based image analysis (OBIA) for estimating tree canopy area and aboveground biomass in Miombo woodlands. We randomly established 41 225-m 2 plots in Mukuvisi Woodland, Zimbabwe, and used RGB data with OBIA to estimate tree canopy area in those plots. We also field measured the height, canopy area, and trunk diameter at breast height of all trees that fell in those plots, then used the field data and a published allometric equation to estimate aboveground tree biomass (AGB). OBIA classification accuracy was high (Jaccard similarity index = 0.96). Data analysis showed significant positive linear relationship between AGB and field-measured canopy area (R 2 = 0.87, p <; 0.003), and significant exponential relationships between: 1) OBIA-derived canopy area and AGB (R 2 = 0.56, p <; 0.0001); and 2) field-measured canopy area and OBIA-derived canopy area (R 2 = 0.63, p <; 0.0001), and no significant differences (t = 19.67, df = 78, p = 0.28) between field-measured canopy area (×̅ = 187.11 m 2 , σ = 127.03) and OBIA-derived canopy area (×̅ = 163.00 m 2 , σ = 50.08). We conclude that RGB data with OBIA are suitable for estimating tree canopy area in Miombo woodlands for various low-accuracy purposes (e.g., biomass estimation).
Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories ...across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5–12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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