A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)-namely, vegetation height, vegetation cover, and vertical structural complexity-to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide

Individual tree attribute estimation and uniformity assessment in fast-growing Eucalyptus spp. forest plantations using lidar and linear mixed-effects models

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry e ciency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-e ect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-e ects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across di erent stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random e ects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coe cient (GC).We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random e ect variable, and canopy height, crown volume, and crown projected area as fixed e ects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planninginfo:eu-repo/semantics/publishedVersio

Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR

Forest landscape restoration is a global priority to mitigate negative effects of climate change, conserve biodiversity, and ensure future sustainability of forests, with international pledges concentrated in tropical forest regions. To hold restoration efforts accountable and monitor their outcomes, traditional strategies for monitoring tree cover increase by field surveys are falling short, because they are labor-intensive and costly. Meanwhile remote sensing approaches have not been able to distinguish different forest types that result from utilizing different restoration approaches (conservation versus production focus). Unoccupied Aerial Vehicles (UAV) with light detection and ranging (LiDAR) sensors can observe forests` vertical and horizontal structural variation, which has the potential to distinguish forest types. In this study, we explored this potential of UAV-borne LiDAR to distinguish forest types in landscapes under restoration in southeastern Brazil by using a supervised classification method. The study area encompassed 150 forest plots with six forest types divided in two forest groups: conservation (remnant forests, natural regrowth, and active restoration plantings) and production (monoculture, mixed, and abandoned plantations) forests. UAV-borne LiDAR data was used to extract several Canopy Height Model (CHM), voxel, and point cloud statistic based metrics at a high resolution for analysis. Using a random forest classification model we could successfully classify conservation and production forests (90% accuracy). Classification of the entire set of six types was less accurate (62%) and the confusion matrix showed a divide between conservation and production types. Understory Leaf Area Index (LAI) and the variation in vegetation density in the upper half of the canopy were the most important classification metrics. In particular, LAI understory showed the most variation, and may help advance ecological understanding in restoration. The difference in classification success underlines the difficulty of distinguishing individual forest types that are very similar in management, regeneration dynamics, and structure. In a restoration context, we showed the ability of UAV-borne LiDAR to identify complex forest structures at a plot scale and identify groups and types widely distributed across different restored landscapes with medium to high accuracy. Future research may explore a fusion of UAV-borne LiDAR with optical sensors , include successional stages in the analyses to further characterize , distinguish forest types and their contributions to landscape restoration

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain

Treetop: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists

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

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models esti-mating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems

Avaliação da degradação e restauração de florestas tropicais através de sensoriamento remoto lidar

The present study investigates new frontiers of lidar technology knowledge assessessing of tropical forest degradation and restoration. The thesis is structured with an introductory chapter, four technical chapters, which explored technical and scientific aspects of the application of lidar technology to the evaluation of forest degradation in the Amazon and restoration of forests in the Atlantic Forest, and a final chapter with considerations and a summary of the main scientific results obtained in this thesis. The results of chapter 2 provided important insights for the correct modeling of leaf area density (LAD) proflies profiles usind airborne lidar. LAD profile is the decomposition of the leaf area index (LAI) along the vertical canopy profile and can be used to answer many ecological questions. The results of chapter 2 provided important insights for the correct modeling of LAD profiles. Chapter 3, using lidar data on aerial and portable ground platforms, in the Biological Dynamics of Forest Fragments Project (BDFFP), demonstrated in an unprecedented way the effect of forest fragmentation in the canopy structure (lidar-derived) and their relationships with the change of the tree community. In this chapter, the results showed that the lidar technology has enormous potential to monitor the impact of forest fragmentation in a high precision scale for large areas. Chapter 4, using data from several forest typologies in the Atlantic Forest biome restoration collect by a portable ground lidar system, demonstrated the potential of canopy structural attributes to distinguish different forest typologies and to estimate above ground woody dry biomass. However, the results were not positive for estimating tree community diversity (richness, Shannon index and species composition). Finally, chapter 5 demonstrated the potential of a novel lidar system on a drone platform (also known as UAV - unmanned aerial vehicle) to monitor forest restoration plantations. Lidar is revolutionizing the way we measure forest landscapes and can be an indispensable tool for the success of forest restoration projects, having the potential to support on planning, monitoring and inspection of forest restoration landscapes. In this thesis, we demonstrate several applications of remote sensing to address the context of forest restoration, and we established methodological bases for other studies to expand the use of this technology for decision making in tropical forest conservation, management and restoration.O presente estudo investiga novas fronteiras do conhecimento da aplicação da tecnologia de sensoriamento remoto lidar à avaliação da degradação e restauração de florestas tropicais. A tese está estruturada na forma de um capítulo de introdução, quatro capítulos técnicos, que exploraram aspectos técnicos e científicos da aplicação da tecnologia lidar à avaliação da degradação de florestas na Amazônia e restauração de florestas na Mata Atlântica, e de um capítulo final com considerações gerais e uma síntese dos principais resultados científicos obtidos nesta tese. O capítulo 2, utilizando dados lidar aeroembarcados em avião, analisou uma questão técnica, sobre a influência da densidade de pulsos da nuvem lidar e da resolução de amostragem para a modelagem do perfil de densidade de áerea foliar em florestas tropicais (DAF). O perfil de DAF é a decomposição do índice de área foliar (IAF) ao longo do perfil vertical do dossel e pode ser utilizado para responder diversas questões ecológicas. Os resultados da capítulo 2 trouxeram importantes insights para a correta modelagem dos perfis de DAF. O capítulo 3, utilizando dados lidar em plataformas aeroembarcados e terrestre portátil, no Projeto Dinâmica Biologica de Fragmentos Florestais (PDBFF), demonstrou de maneira inédita o efeito da fragmentação florestal sobre a alteração da estrutura do dossel (derivados de dados lidar) e suas relações com a mudança da comunidade arbórea. Neste capítulo os resultados demonstraram que a tecnologia lidar tem enorme potencial para monitorar o impacto da fragmentação florestal para grandes áreas e em fina escala. O capítulo 4, utilizando dados de diversas tipologias florestais em restauração no bioma Mata Atlântica, a partir de um sistema lidar terrestre portátil, demonstrou a capacidade dos atributos estruturais do dossel em distinguir diferentes tipologias florestais, estimar diversidade e biomassa de madeira acima do solo. Contudo, os resultados não foram muito positivos para estimativa da diversidade da comunidade arbórea (riqueza, indice de Shannon e composição de espécies). Finalmente, o capitulo 5 demonstrou a capacidade de um sistema inovador lidar aeroembarcado em uma plataforma drone (também conhecida como VANT - veículo aéreo não tripulado) para monitorar plantios de restauração florestal. O lidar está revolucionando a maneira de mensurarmos as paisagens florestais, podendo ser uma ferramenta imprecindível para o sucesso dos projetos de restauração florestal em larga escala, tendo o potencial de auxiliar desde o planejamento ao monitoramento e fiscalização dos projetos florestais. Nesta tese, demonstramos diversas aplicações do sensoriamento remoto lidar ao contexto da restauração florestal, e estabelecemos bases metodológicas para que outros estudos expandam o uso desta tecnologia para tomada de decisão na conservação, manejo e restauração de florestas tropicias

Susceptibility and fire damage on a flooded forest (igapo) and upland forest in the Central Amazon accessed with portable ground lidar

Nutrient-poor and seasonally flooded Amazon forests have suffered high impacts from forest fires. During the dry periods, seasonally flooded forests can present higher air temperature and lower humidity compared to upland forests, enabling the occurrence and spread of fire. These microclimatic conditions may be related to structural attributes of the forest (canopy gap fraction, height and density of understory) that favor changing the microclimate. A high densityof vegetation in the understory (with wet vegetation) can control the spread of fire and the forest canopy height and opening can control the stability of the microclimate inside the forest.The aim of this study was to evaluate these attributes to determine susceptibility and impacts of fires in these forests. To estimate these forest structure attributes we used a portable active remote sensing system equipped with LiDAR RIEGL LD90-3100VHS-FLP. The operation of the data collection in the field is quick and easy, compared to other systems that also use LiDAR device. Forest structural attributes, such as the leaf area density (LAD) along the vertical profile of the forest were extracted from two-dimensional clouds of first and last returns at 2000 Hz. The pulse emitted by the equipment, with a wavelength of 900 nm (near infrared spectrum)is strongly reflected by leaves. The instrument was mounted on a gimbal to maintain a zenith shot angle and it was carried 1m above the ground. Ten transects of 250m length were, sampled at constant speed for each situation: (1) unburned flooded forest (2) burned flooded forest (3) unburned upland forest and (4) burned upland forest. The flooded forest showed a more damage after fire, with loss of 71% of this vegetation, and also it was more susceptible to fire occurrence due to the higher gap fraction (at least two times higher than the upland increasing the entry of sunlight). Canopy height was 15%lower (what makes flooded forests more vulnerable to the external environment) and the density of the understory, 43%lower (less living moist vegetation to limit the spread of fire). The flooded forest are extremely fragile to fires, being more susceptible to occur, with higher post fire damage and lower regeneration, which is hard by flooding period.As florestas sazonalmente alagáveis por águas pretas e pobres em nutrientes (igapó) têm sofrido altos impactos por incêndios florestais. Durante os períodos secos estas florestas apresentam menores extremos de umidade relativa do ar e maioresextremos de temperatura comparada com a terra-firme, favorecendo a proliferação do fogo. Estas características microclimáticas podem ser controladas por atributos estruturais da floresta (abertura de dossel, altura da floresta e densidade de sub-bosque). O sub-bosque mais denso (com vegetação úmida) controla a proliferação do fogo. A altura da floresta e a abertura de dossel controlam a estabilidade do microclima no interior da floresta. O objetivo do estudo foi avaliar estes três atributos (densidade de sub-bosque, altura da floresta e abertura de dossel) como determinantesda susceptibilidade aos incêndios florestais e os impactos pós-fogo nessas florestas. Foi utilizadoum sistema portátil de sensoriamento remoto ativo equipado com LiDAR RIEGL LD90-3100VHS-FLP para estimar os atributos. A coleta dos dados em campo é rápida e fácil, comparada com outros sistemas que também utilizam o LiDAR. Os atributos das florestas são extraídos de nuvens bidimensionais com primeiros e últimos retornos (2000Hz), onde pode ser estimado por exemplo a densidade de área foliar (LAD) ao longo do perfil vertical da floresta. O pulso emitido pelo equipamento, com comprimento de onda de 900nm (espectro infravermelho próximo) é fortemente refletido pelas folhas. O instrumento é montado em um gimbal mantido na orientação do zênite e é carregado à um metro acima do solo. Dez transectos de 250 m foram percorridos em velocidade constante para cada situação: (1) igapó não queimado, (2) igapó queimado, (3) terra-firme não queimada e (4) terra-firme queimada. O igapó apresentou maiores danos pós-fogo, com perda de 71% de sua vegetação, e também maior susceptibilidade à ocorrência de incêndios devido a abertura de dossel, duas vezes maior que a terra-firme (aumentando a iluminação solar), altura da floresta 15% mais baixa (maior vulnerabilidade de alteração do microclima) e 43% menos vegetação no sub-bosque (menos vegetação úmida que dificulta a proliferação do fogo). As florestas de igapó são fitofisionomias extremamente frágeis aos incêndios, sendo mais susceptíveis à ocorrência de incêndios, com maiores danos pós fogo e menor regeneração, que é dificuldade pelo período de alagamento