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Terrestrial LiDAR has emerged as a promising technology for accurate forest assessment. LiDAR can provide a 3D image composed of a cloud of points using a rotary laser scanner. The point cloud data (PCD) contain information on the (x, y, z) coordinates of every single scanned point and a raw intensity parameter. This study introduces an algorithm for the automatic and accurate separation of the photosynthetic features of a PCD. It is shown that the recorded raw intensity is not a suitable parameter for the separation of photosynthetic features. Instead, for the first time, the absorption intensity is developed for every point based on its raw intensity and distance from the scanner, using proper scaling functions. Then, the absorption intensity is utilized as the only criterion for the classification of the points between photosynthetic and non-photosynthetic features. The proposed method is applied to the scans from a Canadian Boreal Forest and successfully extracted the photosynthetic features with minimal average type I and type II error rates of 5.7% and 4.8%. The extracted photosynthetic PCD can be readily used for calculating important forest parameters such as the leaf area index (LAI) and the green biomass. In addition, it can be used for estimating forest carbon storage and monitoring temporal changes in vegetation structure and ecosystem health. [ABSTRACT FROM AUTHOR]

Chronosequence changes among Tropical Dry Forests (TDFs) are essential for understanding this unique ecosystem, which is characterized by its seasonality (wet and dry) and a high diversity of deciduous trees and shrubs. From 2005 to 2021, we used two different airborne LiDAR systems to quantify structural changes in the forest at Santa Rosa National Park. Line- and shape-based waveform metrics were used to record the overall changes in the TDF structure. Based on a 16-year growth analysis, notable variations in height-related profiles were observed, particularly for RH50, RH100, and waveform-produced canopy heights. The results showed that Cy and RG have increased since the forests have been growing, whereas Cx has decreased. The decrease in Cx is because ground returns are lower when the canopy density i and canopy height increase. A positive relationship was observed between Cy and CH, RG, and RH100, particularly for the wet season data collected in 2021. These findings provide important insights into the growth dynamics of TDFs in Santa Rosa National Park and could inform future conservation efforts. [ABSTRACT FROM AUTHOR]

Secondary succession is defined as natural regeneration following complete forest clearance from anthropogenic or natural disturbances. Traditional strategies aimed to map and characterize secondary succession using remote sensing are usually based on deterministic approaches, where transitions between successional stages are not considered. These transitions represent rich environments between successional stages and play a key role in ecosystem regeneration. Here, we evaluate the use of the Full-waveform Airborne LiDAR to characterize changes in forest structure between the transition of early-to-intermediate and intermediate-to-late forest succession at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. The vertical forest structure was analyzed on twenty cross-sections selected between forest transitions previously mapped using machine learning; leaf area density (LAD) and waveform metrics were studied based on the waveform profile derived from twenty-seven plots distributed in different successional forest patches. Results suggest that LiDAR techniques can identify forest structure differences between successional stages and their transitions. The significance proves that transitions exist, highlights the unique transitional characteristics between intermediate and late successional stages and contributes to understanding the significance of inter-successional stages (transitions) in secondary dry forests. [ABSTRACT FROM AUTHOR]

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs. [ABSTRACT FROM AUTHOR]

In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the apprication of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems. Index Terms--Segmentation, stereo, 3D fitting, scene analysis, image understanding, forestry inventory.

This study investigates the usefulness of thermal infrared reflectance spectroscopy (TIR) to map rock faces in an underground environment. A laboratory Fourier transform infrared (FTIR) spectrometer was used to measure reflectance spectra (500-5000 [cm.sup.-1]) of freshly broken rock surfaces. A total of 37 samples covering 10 different rock types, including barren silicate rocks and rocks with disseminated sulfides, was collected from eight mines around the Sudbury Basin in Ontario. Freshly broken rock surfaces were measured wet and dry to address environmental conditions encountered underground. Spectral data were processed by the second-derivative approach and the spectral angle mapper algorithm (SAM) for rock-type identification. An average of 77% accuracy was achieved for all rock types. The success of this effort implies that hyperspectral analysis using second-derivative spectra is effective to remove the effect of liquid water, local geometry, and disseminated sulfides while preserving diagnostic rock spectral signatures between 700 and 1300 [cm.sup.-1]. Keywords: rock cores, spectroscopy, thermal, underground mining.

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every endmember can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping. Index Terms--Hyperspectral, spectral derivative, unmixing.

Sensitivity of forest mortality to drought in carbon‐dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high‐frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site‐year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave‐one‐out cross‐validation R2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine‐scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high‐resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought. [ABSTRACT FROM AUTHOR]

Even though existing remote-sensing-based drought indices are widely used in many different types of ecosystems, their utility has not been widely assessed in tropical dry forests (TDFs). The aim of this study is to evaluate the performance of three remote-sensing-based drought indices, the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), for meteorological drought monitoring in TDFs using the moderate-resolution imaging spectroradiometer (MODIS) products. The correlation between the VCI, TCI, and VHI and multiple time scales of the Standardized Precipitation Index (SPI) (1, 3, 6, 9, 12, 15, 18, 21, 24 months) for each month (January to December) and each season (dry season, dry-to-wet season, wet season and wet-to-dry season) were conducted using the Pearson correlation analysis. We also correlated year-to-year changes of satellite-based drought indices with the changes of the in situ annual SPI (A_SPI) which provides annual information on the mean meteorological drought. The analysis reveals that the ability of these remote-sensing-based drought indices for meteorological drought monitoring varies with timing, and the TCI outperforms the VCI and VHI in terms of seasonal and annual scale. These remote-sensing indices performed well in monitoring meteorological drought in the dry season, poorly in the in the dry-to-wet season, and moderately in the wet season. The TCI performed best in monitoring meteorological drought in the wet-to-dry period, followed by VHI, whereas the VCI performed worst. All of these remote-sensing-based drought indices failed to detect drought in May during the green-up period and in September, October, and November when the water content in the root regions was abundant. Our results indicate that the evapotranspiration of TDFs is more sensitive than canopy greenness to detect meteorological drought. Results from this study increase the ability to provide real-time drought monitoring and early warnings of drought in TDFs. [ABSTRACT FROM AUTHOR]