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Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. The authors thank Nicholas L. Crookston for co‐developing the web‐LiDAR treetop tool, and the two anonymous reviewers for their helpful suggestions on the first version of the manuscript. This study is based on the work supported by the Department of Defence Strategic Environmental Research and Development Program (SERDP) under grants No. RC‐2243, RC19‐1064 and RC20‐1346 and USDA Forest Service (grand No. PRO00031122)

The analysis of land use and land cover data provides invaluable support to a variety of land management and conservation activities. However, historically its application has been limited by high costs associated with data acquisition, analysis, and classification. In recent years, freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. Our results demonstrate the usefulness of this approach for conducting land use and land cover analysis using much less time, money, and human resources. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data are limited.

Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes.

An objective, quantifiable index of structural biodiversity that could be rapidly obtained with reduced or no field effort is essential for the use of structure as universal ecological indicator for ecosystem management. Active remote sensing provides a rapid assessment tool to potentially guide land managers in highly dynamic and spatially complex landscapes. These landscapes are often dependent on frequent disturbance regimes and characterized by high endemism. We propose a modified Shannon–Wiener Index and modified Evenness Index as stand structural complexity indices for surrogates of ecosystem health. These structural indices are validated at Tall Timbers Research Station the site of one of the longest running fire ecology studies in southeastern U.S. This site is dominated by highly dynamic pine-grassland woodlands maintained with frequent fire. Once the dominant ecosystem in the Southeast, this woodland complex has been cleared for agriculture or converted to other cover types, and depends on a frequent (1- to 3-year fire return interval) low- to moderate-intensity fire regime to prevent succession to mixed hardwood forests and maintain understory species diversity. Structural evaluation of the impact of multiple disturbance regimes included height profiles and derived metrics for five different fire interval treatments; 1-year, 2-year, 3-year, mixed fire frequency (a combination of 2- and 4-year fire returns), and fire exclusion. The 3-dimensional spatial arrangement of structural elements was used to assess hardwood encroachment and changes in structural complexity. In agreement with other research, 3-year fire return interval was considered to be the best fire interval treatment for maintaining the pine-grassland woodlands, because canopy cover and vertical diversity indices were shown to be statistically higher in fire excluded and less frequently burned plots than in 1- and 2-year fire interval treatments. We developed a LiDAR-derived structural diversity index, LHDI, and propose that an ecosystem-specific threshold target for management intervention can be developed, based on significant shifts in structure and composition using this new index. Structural diversity indices can be valuable surrogates of ecosystem biodiversity, and ecosystem-specific target values can be developed as objective quantifiable goals for conservation and ecosystem integrity, particularly in remote areas.

This study used an affordable ground-based portable LiDAR system to provide an understanding of the structural differences between old-growth and secondary-growth Southeastern pine. It provided insight into the strengths and weaknesses in the structural determination of portable systems in contrast to airborne LiDAR systems. Portable LiDAR height profiles and derived metrics and indices (e.g., canopy cover, canopy height) were compared among plots with different fire frequency and fire season treatments within secondary forest and old growth plots. The treatments consisted of transitional season fire with four different return intervals: 1-yr, 2-yr, 3-yr fire return intervals, and fire suppressed plots. The remaining secondary plots were treated using a 2-yr late dormant season fire cycle. The old growth plots were treated using a 2-yr growing season fire cycle. Airborne and portable LiDAR derived canopy cover were consistent throughout the plots, with significantly higher canopy cover values found in 3-yr and fire suppressed plots. Portable LiDAR height profile and metrics presented a higher sensitivity in capturing subcanopy elements than the airborne system, particularly in dense canopy plots. The 3-dimensional structures of the secondary plots with varying fire return intervals were dramatically different to old-growth plots, where a symmetrical distribution with clear recruitment was visible. Portable LiDAR, even though limited to finer spatial scales and specific biases, is a low-cost investment with clear value for the management of forest canopy structure.

Advances in remote sensing and space-based imaging have led to an increased understanding of past settlements and landscape use, but e until now e the images in tropical regions have not been detailed enough to provide datasets that permitted the computation of digital elevation models for heavily forested and hilly terrain. The application of airborne LiDAR (light detection and ranging) remote sensing provides a detailed raster image that mimics a 3-D view (technically, it is 2.5-D) of a 200 sq km area covering the settlement of Caracol, a long-term occupied (600 BC-A.D. 250e900) Maya archaeological site in Belize, literally “seeing” though gaps in the rainforest canopy. Penetrating the encompassing jungle, LiDAR-derived images accurately portray not only the topography of the landscape, but also, structures, causeways, and agricultural terraces e even those with relatively low relief of 5e30 cm. These data demonstrate the ability of the ancient Maya to modify, radically, their landscape in order to create a sustainable urban environment. Given the time and intensive effort involved in producing traditional large-scale maps, swath mapping LiDAR is a powerful cost-efficient tool to analyze past settlement and landscape modifications in tropical regions as it covers large study areas in a relatively short time. The use of LiDAR technology, as illustrated here, will ultimately replace traditional settlement mapping in tropical rainforest environments, such as the Maya region, although ground verification will continue to be necessary to test its efficacy.

The structure of a forest canopy often reflects its disturbance history. Such signatures of past disturbances or legacies can influence how the ecosystem functions across broad spatio-temporal scales. The 1938 hurricane and ensuing salvage operations which swept through New England represent the most recent large, infrequent disturbance (LID) in this region. Though devastating (downing ∼ 70% of the timber at Harvard Forest), the disturbance was not indiscriminate; it left behind a heterogeneous landscape comprised of different levels of canopy damage. We analyzed large-footprint LiDAR, from the Prospect Hill tract at Harvard Forest in central Massachusetts, to assess whether damage to the forest structure from the hurricane and subsequent timber extraction could be discerned after ∼ 65 years. Differences in LiDAR-derived measures of canopy height and vertical diversity were a function of the degree of damage from the 1938 hurricane and the predominant tree species which is, in part, a function of land use history. Higher levels of damage corresponded to slightly shorter canopies with a less even vertical distribution of return from the ground to the top. In addition, differences in canopy topography as revealed by spatial autocorrelation of canopy top heights were found among the damage classes. Less disturbed stands were characterized by lower levels of local autocorrelation for canopy height and higher levels of vertical diversity of LiDAR returns. These differences in canopy structure reveal that the forest tract has not completely recovered from the 1938 LID and salvage regime, which may have implications on arboreal and understory habitat and other ecosystem functions.

Carbon estimates from terrestrial ecosystem models are limited by large uncertainties in the current state of the land surface. Natural and anthropogenic disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect or assess with conventional methods. In this study, we combined two recent advances in remote sensing and ecosystem modeling to improve model carbon stock and flux estimates at a tropical forest study site at La Selva, Costa Rica (10°25′ N, 84°00′ W). Airborne lidar remote sensing was used to measure spatial heterogeneity in the vertical structure of vegetation. The ecosystem demography model (ED) was used to estimate the consequences of this heterogeneity for regional estimates of carbon stocks and fluxes. Lidar data provided substantial constraints on model estimates of both carbon stocks and net carbon fluxes. Lidar-initialized ED estimates of aboveground biomass were within 1.2% of regression-based approaches, and corresponding model estimates of net carbon fluxes differed substantially from bracketing alternatives. The results of this study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model. Extending these analyses to larger scales will require the development of regional and global lidar data sets, and the continued development and application of height structured ecosystem models.

Aim Previous studies have developed strong, site-specific relationships between canopy metrics from lidar (light detecting and ranging) remote sensing data and forest structural characteristics such as above-ground biomass (AGBM), but the generality of these relationships is unknown. In this study, we examine the generality of relationships between lidar metrics and forest structural characteristics, including AGBM, from two study areas in Central America with different precipitation patterns. Location A series of tropical moist forest sites in Panama and a tropical wet forest in Costa Rica. Methods Canopy metrics (e.g. canopy height) were calculated from airborne lidar data. Basal area, mean stem diameter and AGBM were calculated from measurements taken as a part of ongoing forest dynamics studies in both areas. We examined the generality of relationship between lidar metrics and forest structure, and possible environmental effects (e.g. leaf phenology). Results We found that lidar metrics were strongly correlated ( R 2 : 0.65‐0.92) with mean stem diameter, basal area and AGBM in both regions. We also show that the relationships differed between these regions. Deciduousness of canopy trees in the tropical moist forest area accounted for the differences in predictive equations for stem diameter and basal area. The relationships between lidar metrics and AGBM, however, remained significantly different between the two study areas even after adjusting for leaf drop. We attribute this to significant differences in the underlying allometric relationships between stem diameter and AGBM in tropical wet and moist forests. Conclusions Important forest structural characteristics can be estimated reliably across a variety of conditions sampled in these closed-canopy tropical forests. Environmental factors such as drought deciduousness have an important influence on these relationships. Future efforts should continue to examine climatic factors that may influence the generality of the relationships between lidar metrics and forest structural characteristics and assess more rigorously the generality of field-derived allometric relationships.

Accurate estimates of the total biomass in terrestrial vegetation are important for carbon dynamics studies at a variety of scales. Although aboveground biomass is difficult to quantify over large areas using traditional techniques, lidar remote sensing holds great promise for biomass estimation because it directly measures components of canopy structure such as canopy height and the vertical distribution of intercepted canopy surfaces. In this study, our primary goal was to explore the sensitivity of lidar to differences in canopy structure and aboveground biomass in a dense, neotropical rainforest. We first examined the relationship between simple vertical canopy profiles derived from field measurements and the estimated aboveground biomass (EAGB) across a range of field plots located in primary and secondary tropical rainforest and in agroforesty areas. We found that metrics from field-derived vertical canopy profiles are highly correlated (R 2 up to .94) with EAGB across the entire range of conditions sampled. Next, we found that vertical canopy profiles from a large-footprint lidar instrument were closely related with coincident field profiles, and that metrics from both field and lidar profiles are highly correlated. As a result, metrics from lidar profiles are also highly correlated (R 2 up to .94) with EAGB across this neotropical landscape. These results help to explain the nature of the relationship between lidar data and EAGB, and also lay the foundation to explore the generality of the relationship between vertical canopy profiles and biomass in other tropical regions. D 2002 Elsevier Science Inc. All rights reserved.

Quantification of forest structure is important for developing a better understanding of how forest ecosystems function. Additionally, estimation of forest structural attributes, such as aboveground biomass (AGBM), is an important step in identifying the amount of carbon in terrestrial vegetation pools and is central to global carbon cycle studies. Although current remote sensing techniques recover such tropical forest structure poorly, new large-footprint lidar instruments show great promise. As part of a prelaunch validation plan for the Vegetation Canopy Lidar (VCL) mission, the Laser Vegetation Imaging Sensor (LVIS), a large-footprint airborne scanning lidar, was flown over the La Selva Biological Station, a tropical wet forest site in Costa Rica. The primary objective of this study was to test the ability of large-footprint lidar instruments to recover forest structural characteristics across a spectrum of land cover types from pasture to secondary and primary tropical forests. LVIS metrics were able to predict field-derived quadratic mean stem diameter (QMSD), basal area, and AGBM with R 2 values of up to .93, .72, and .93, respectively. These relationships were significant and nonasymptotic through the entire range of conditions sampled at the La Selva. Our results confirm the ability of large-footprint lidar instruments to estimate important structural attributes, including biomass in dense tropical forests, and when taken along with similar results from studies in temperate forests, strongly validate the VCL mission framework. D 2002 Elsevier Science Inc. All rights reserved.

Spatial patterns of forest canopies are fractal as they exhibit variation over a continuum of scales. A measure of fractal dimension of a forested landscape represents the spatial summation of physiologic (leaf-level), demographic (populationlevel), and abiotic (e.g., edaphic) processes, as well as exogenous disturbances (e.g., fire and hurricane) and thus provides a basis to classify or monitor such systems. However, forests typically exhibit a spectrum of fractal parameters which yields further insight to the geometric structure of the system and potentially the underlying processes. We calculated multifractal properties of longleaf pine flatwoods, the predominant ecosystem of central Florida, from canopy profile data derived from an airborne laser altimeter and ground-based measurements in The Nature Conservancy’s Disney Wilderness Preserve located near Kissimmee, Florida. These metrics were compared for six 500 m transects to determine the level of consistency between remotely sensed and field measures and within a forest community. Multifractal techniques uncovered subtle differences between transects that could correspond to unique, underlying abiotic and biotic processes. These techniques should be considered a valuable tool for ecological analysis. # 2000 Elsevier Science B.V. All rights reserved.

Methods for predicting the ecological impacts of land use change on biodiversity and ecosystem function are needed to guide land planning and resource management decisions. This study explores the consequences of alternative scenarios of land cover change on the abundance and arrangement of potential habitat for a suite of species in the Little Tennessee River Basin (LTRB) in the Southern Appalachian Highlands and the Hoh River Basin (HORB) on the Olympic Peninsula. We addressed two questions: (1) How does land ownership affect the availability of suitable habitat for a variety of species in changing landscapes (and how do restrictions on forest harvest then change habitat availability)? (2) Are species differentially affected by land cover changes that vary among landowners? Scenarios of land cover change were projected by using a spatially explicit model in which the probability of land being converted from one cover type to another was conditional upon social, economic, and ecological factors. Potentia...

[1] Tropical forest biomass estimation based on the structure of the canopy is a burgeoning and crucial remote sensing capability for balancing terrestrial carbon budgets. This paper introduces a new approach to structural biomass estimation based on the Fourier transform of vertical profiles from lidar or interferometric SAR (InSAR). Airborne and field data were used from 28 tropical wet forest stands at La Selva Biological Station, Costa Rica, with average biomass of 229 Mg-ha−1. RMS scatters of remote sensing biomass estimates about field measurements were 58.3 Mg-ha−1, 21%, and 76.1 Mg-ha−1, 26%, for lidar and InSAR, respectively. Using mean forest height, the RMS scatter was 97 Mg-ha−1, ≈34% for both lidar and InSAR. The confidence that Fourier transforms are a significant improvement over height was >99% for lidar and ≈90% for InSAR. Lidar Fourier transforms determined the useful range of vertical wavelengths to be 14 m to 100 m.

[1] This paper addresses the estimation of vertical vegetation density profiles from multibaseline interferometric synthetic aperture radar (InSAR) data from the AirSAR aircraft at C band over primary, secondary, and abandoned-pasture stands at La Selva Biological Station, Costa Rica in 2004. Profiles were also estimated from field data taken in 2006 and lidar data taken with the LVIS, 25 m spot instrument in 2005. After motivating the study of tropical forest profiles based on their role in the global carbon cycle, ecosystem state, and biodiversity, this paper describes the InSAR, field, and lidar data acquisitions and analyses. Beyond qualitative agreement between profiles from the 3 measurement techniques, results show that InSAR and lidar profile-averaged mean height have RMS scatters about field-measured means of 3.4 m and 3.2 m, 16% and 15% of the average mean height, respectively. InSAR and lidar standard deviations of the vegetation distribution have RMS scatters about the field standard deviations of 1.9 m and 1.5 m, or 27% and 21%, respectively. Dominant errors in the profile-averaged mean height for each measurement technique were modeled. InSAR inaccuracies, dominated by ambiguities in finding the ground altitude and coherence calibration, together account for about 3 m of InSAR error in the mean height. The dominant, modeled error for the field measurements was the inaccuracy in modeling the trees as uniformly filled volumes of leaf area, inducing field errors in mean height of about 3 m. The dominant, modeled lidar error, also due to finding the ground, was 2 m.

Vertical profiles of forest density potentially are robust indicators of forest biomass, fire susceptibility and ecosystem function. Tropical forests, which are among the most dense and complicated targets for remote sensing, contain about 45% of the world's biomass. Remote sensing of tropical forest structure is therefore an important component to global biomass and carbon monitoring. As in radio astronomy, which uses multibaseline radio interferometry to measure the structure of celestial objects, so multibaseline interferometric SAR (InSAR) can be used to estimate the vertical structure of forests. Vegetation density profiles, along with radar backscattering characteristics and attenuation, determine the radar brightness profile "seen" by InSAR. This paper will describe an experiment at La Selva Biological Station in Costa Rica (~3m rainfall/year) in which we flew 18 effective fixed baselines over tropical forests at C-band (0.056 m wavelength) and L-band (0.25 m). Preliminary inversions for radar brightness profiles will be compared to extensive lidar profiles measured in the same area. They will also be compared to field-measured profiles.

An experimental profiling airborne laser altimeter system developed at NASA's Goddard Space Flight Center was used to acquire vertical canopy data from several ecosystem types from The Nature Conservancy's Disney Wilderness Preserve, near Kissimmee, Florida. This laser altimeter, besides providing submeter accuracy of tree height, captures a profile of data which relates to the magnitude of reflectivity of the laser pulse as it penetrates different elevations of the forest canopy. This complete time varying amplitude of the return signal of the laser pulse, between the first (i.e., the canopy top) and last (i.e., the ground) returns, yields a waveform which is related to canopy architecture, specifically the nadir-projected vertical distribution of the surface of canopy components (i.e., foliage, twigs, and branches). Selected profile returns from representative covertypes (e.g., pine flatwoods, bayhead, and cypress wetland) were compared with ground truthed forest composition (i.e., species and size class distribution) and structural (i.e., canopy height, canopy closure, crown depth) measures to help understand how these properties contribute to variation in the altimeter waveform.