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Introduction: Although wildlife crossing structures have proven successful at reducing wildlife-vehicle collisions and linking fragmented habitat, their ability to prevent electrocutions of arboreal wildlife has not been closely examined. Objective: To evaluate the effectiveness of aerial rope bridges in restoring habitat connectivity for arboreal species in Manuel Antonio, Costa Rica, while preventing electrocutions by determining 1) what species are using the rope bridges and 2) whether wildlife prefer to use rope bridges instead of other hazardous structures that cross the roads (such as telephone cables, which are often in close proximity to electric wires). Methods: From January to May 2016, nine rope bridges along the highly-trafficked main road that extends from Quepos to Manuel Antonio, Costa Rica, were monitored using camera traps, and ten rope bridges were observed directly along a paved side road off the main road. Results: A total of 11 species were seen using the bridges, and 1 540 crossings were witnessed via camera traps and observations (1 234 via camera traps, 306 during observations). Results from a paired t-test showed no significant difference in the average number of individuals crossing the road via rope bridges versus telephone cables (t(8) = 1.027, P = 0.334). Conclusions: Rope bridges are used by a variety of arboreal wildlife species with a high degree of frequency; however, due to the equally high usage of telephone cables by arboreal wildlife, they are insufficient to prevent wildlife electrocutions on their own. Rope bridges should be installed in tandem with other methods to prevent electrocutions, such as insulating electric wires, to facilitate the safe passage of wildlife over roads. Resumen Introducción: Aunque los pasos de fauna han demostrado ser exitosos para reducir las colisiones entre vehículos y vida silvestre y vincular el hábitat fragmentado, su capacidad para prevenir electrocuciones de la vida silvestre arbórea no se ha examinado a fondo. Objetivo: Evaluar la efectividad de los puentes aéreos de cuerdas para restaurar la conectividad del hábitat de las especies arbóreas en Manuel Antonio, Costa Rica y al mismo tiempo prevenir las electrocuciones al determinar 1) qué especies están usando los puentes de cuerda y 2) si la vida silvestre prefiere usar puentes de cuerda en lugar de otras estructuras peligrosas que cruzan las carreteras (como cables telefónicos, que frecuentemente están muy cerca de cables eléctricos). Métodos: De enero a mayo de 2016, se monitorearon nueve puentes de cuerda a lo largo de la carretera principal altamente transitada que se extiende desde Quepos a Manuel Antonio, Costa Rica, utilizando cámaras trampa y la observación directa en diez puentes de cuerda a lo largo de una carretera pavimentada más pequeña fuera de la carretera principal. Resultados: Se observaron un total de 11 especies utilizando los puentes y se presenciaron 1 540 cruces mediante cámaras trampa y observaciones (1 234 mediante cámaras trampa, 306 durante las observaciones). Los resultados de una prueba t pareada no mostraron diferencias significativas en el número promedio de individuos que cruzan la carretera a través de puentes de cuerda versus cables telefónicos, t (8) = 1.027, P = 0.334. Conclusiones: Los puentes de cuerdas son utilizados por una variedad de especies de vida silvestre arbóreas con un alto grado de frecuencia; sin embargo, debido al uso igualmente elevado de cables telefónicos por parte de la vida silvestre arbórea, se considera que son insuficientes para prevenir las electrocuciones de la vida silvestre por sí solas. Los puentes de cuerda deben instalarse junto con otros métodos para evitar electrocuciones, como cables eléctricos aislados, para facilitar el paso seguro de la vida silvestre por las carreteras.

Constructed landscapes are composed of diverse communities, representing different social strata and perspectives of a place. In turn, the risks associated with inhabiting unpredictable environment...

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables? relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven secondgrowth and two old-growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human-managed tropical landscapes can now be better characterized. Drone-lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration. Made available in DSpace on 2020-08-01T11:12:46Z (GMT). No. of bitstreams: 1 27015.pdf: 1386408 bytes, checksum: cce9ebab5fed640e715ad6387e973c5a (MD5) Previous issue date: 2020

Spectroscopy plays a key role in Earth observations, especially for studies involving vegetation function and structure. These measurements are critical in the context of carbon cycle monitoring fr...

Determining mechanisms of plant establishment in ecological communities can be particularly difficult in disturbance-dominated ecosystems. Longleaf pine (Pinus palustris Mill.) and its associated plant community exemplify systems that evolved with disturbances, where frequent, widespread fires alter the population dynamics of longleaf pine within distinct life stages. We identified the primary biotic and environmental conditions that influence the survival of longleaf pine in this disturbance-dominated ecosystem. We combined data from recruitment surveys, tree censuses, dense lidar point clouds, and a forest-wide prescribed fire to examine the response of longleaf pine individuals to fire and biotic neighborhoods. We found that fire temperatures increased with increasing longleaf pine neighborhood basal area and decreased with higher oak densities. There was considerable variation in longleaf pine survival across life stages, with lowest survival probabilities occurring during the bolt stage and not in the earlier, more fire-resistant grass stage. Survival of grass-stage, bolt-stage, and sapling longleaf pines was negatively associated with basal area of neighboring longleaf pine and positively related to neighboring heterospecific tree density, primarily oaks (Quercus spp.). Our findings highlight the vulnerability of longleaf pine across life stages, which suggests optimal fire management strategies for controlling longleaf pine density, and—more broadly—emphasize the importance of fire in mediating species interactions.

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 latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Lianas are found in virtually all tropical forests and have strong impacts on the forest carbon cycle by slowing tree growth, increasing tree mortality and arresting forest succession. In a few local studies, ecologists have successfully differentiated lianas from trees using various remote sensing platforms including satellite images. This demonstrates a potential to use remote sensing to investigate liana dynamics at spatio-temporal scales beyond what is currently possible with ground-based inventory censuses. However, why do liana-infested tree crowns and forest stands display distinct spectral signals? And is the spectral signal of lianas only locally unique or consistent across continental and global scales? Unfortunately, we are not yet able to answer these questions, and without such an understanding the limitations and caveats of large-scale application of automated classifiers cannot be understood. Here, we tackle the questions of why we can detect lianas from airborne and spaceborne remote sensing platforms. We identify whether a distinct spectral distribution exists for lianas, when compared to their tree hosts, at the leaf, canopy and stand scales in the solar spectrum (400 to 2500 nm). To do so, we compiled databases of (i) leaf reflectance spectra for over 4771 individual leaves of 539 species, (ii) fine-scale (∼1m2) surface reflectance from 999 tree canopies characterized by different levels of liana infestation in Panama and Malaysia, and (iii) coarse-scale (>100 m2) surface reflectance from hundreds of hectares of heavily infested liana forest stands in French Guiana and Bolivia. Using these data, we find consistent spectral signal of liana-infested canopies across sites with a mean inter-site correlation of 89% (range 74-94%). However, as we find no consistent difference between liana and tree leaves, a distinct liana spectral signal appears to only manifests at the canopy and stand scales (>1m2). To better understand this signal, we implement mechanistic radiative transfer models capable of modeling the vertically stratificatied non-linear mixing of spectral signals intrinsic to lianas infestation of forest canopies. Next, we inversely fit the models to observed spectral signals of lianas at all scales to identify key biochemical or biophysical processes. We then corroborate our model results with field data on liana leaf chemistry and canopy structural properties. Our results suggest that a liana-specific spectral distribution arises due to the combination of cheaply constructed leaves and efficient light interception. A model experiment revealed that the spectral distribution was most sensitive to lower leaf and water mass per unit area, affecting the absorption of NIR and SWIR radiation, and a more planophile (flatter) leaf angle distribution. Finally, we evaluate the theoretical discernibility of lianas from trees and how this varies with remote sensing platforms and resolution. We end by discussing the potential, limitations and risks of applying automated classifiers to detect lianas from remotely sensed data at large scales.

Tropical forests are often located in dicult-to-access areas, which make high-quality forest structure information dicult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-ecient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal dierences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 1.8 vs. 381.2 58 pts/m2). Dierences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 0.09 vs. 0.42 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior. Made available in DSpace on 2020-06-02T04:38:55Z (GMT). No. of bitstreams: 1 27002.pdf: 8268657 bytes, checksum: 92fd75c7b1786acaf00f080246b7eef4 (MD5) Previous issue date: 2020

Remote sensors, both onboard orbital and aircraft platforms or unmanned aerial vehicles (UAVs), are essential for the effective monitoring of forested landscapes. Particularly, UAV systems can generate high-resolution images that provide accurate information on forest stands with or without the need for ground-based data (e.g., calibration or validation) to estimate important forest attributes such as the number of trees, aboveground biomass, or canopy openness (Almeida et al. 2020a, Kotivuori et al. 2020; Ferreira et al. 2020). The development and understanding of the potential of UAV-borne remote sensors are essential for the effective monitoring of forested landscapes in large areas and will play an important role during the UN Decade of Ecosystem Restoration. Here, we explored the use of a UA-borne lidar and hyperspectral (HSI) system to assess the outcomes of a mixed-species restoration plantation experiment designed in a species richness gradient in southeastern Brazil. We used a UAV-lidar-Hyperspectral system to quantify forest structure variables (from lidar) and vegetation indexes (from HSI) and associated these variables with AGB stocks measured in the field and the species richness plantation.

The sustainability and efficiency of ecotourism regarding biodiversity conservation are under constant scrutiny and often dismissed based on negative effects arising from other types of nature-base...

Monitoring ecological restoration has been historically dependent on traditional inventory methods based on detailed information obtained from field plots. New paradigms are now needed to successfully achieve restoration as a large‐scale, long‐lasting transformative process. Fortunately, advances in technology now allow for unprecedented shifts in the way restoration has been planned, implemented, and monitored. Here, we describe our vision on how the use of new technologies by a new generation of restoration ecologists may revolutionize restoration monitoring in the coming years. The success of the many ambitious restoration programs planned for the coming decade will rely on effective monitoring, which is an essential component of adaptive management and accountability. The development of new remote sensing approaches and their application to a restoration context open new avenues for expanding our capacity to assess restoration performance over unprecedented spatial and temporal scales. A new generation of scientists, which have a background in remote sensing but are getting more and more involved with restoration, will certainly play a key role for making large‐scale restoration monitoring a viable human endeavor in the coming decade—the United Nations' decade on ecosystem restoration.

Drone-mounted, high-resolution light detection and ranging reveals the architectural details of an ancient settlement on the Gulf Coast of Florida without parallel in the Southeastern United States. The Raleigh Island shell-ring complex (8LV293) of ca. 900 to 1200 CE consists of at least 37 residential spaces enclosed by ridges of oyster shell up to 4 m tall. Test excavations in 10 of these residential spaces yielded abundant evidence for the production of beads from the shells of marine gastropods. Beads and other objects made from gulf coastal shell were integral to the political economies of second-millennium CE chiefdoms across eastern North America. At places as distant from the coast as the lower Midwest, marine gastropods were imported in raw form and converted into beads and other objects by craftspeople at the behest of chiefs. Bead making at Raleigh Island is exceptional not only for its level of production at the supply end of regional demand but also for being outside the purview of chiefly control. Here we introduce the newly discovered above-ground architecture of Raleigh Island and outline its analytical value for investigating the organization of shell bead production in the context of ancient political economies. The details of shell-ring architecture achieved with drone-mounted LiDAR make it possible to compare the bead making of persons distributed across residential spaces with unprecedented resolution.

We conducted unmanned aerial vehicle lidar missions in the Maya Lowlands between June 2017 and June 2018 to develop appropriate methods, procedures, and standards for drone lidar surveys of ancient Maya settlements and landscapes. Three site locations were tested within upper Usumacinta River region using Phoenix Lidar Systems: Piedras Negras, Guatemala, was tested in 2017, and Budsilha and El Infiernito, both in Mexico, were tested in 2018. These sites represent a range of natural and cultural contexts, which make them ideal to evaluate the usefulness of the technology in the field. Results from standard digital elevation and surface models demonstrate the utility of deploying drone lidar in the Maya Lowlands and throughout Latin America. Drone survey can be used to target and efficiently document ancient landscapes and settlement. Such an approach is adaptive to fieldwork and is cost effective but still requires planning and thoughtful evaluation of samples. Future studies will test and evaluate the methods and techniques for filtering and processing these data.

We are in an unprecedented moment for promoting forest restoration globally, with international and regional pledges to restore at least 350 million hectares by 2030. To achieve these ambitious goals, it is necessary to go beyond traditional plot-scale assessments and develop cost-effective technologies that can monitor the structure and function of restored forests at much broader scales. Lidar remote sensing in unmanned aerial vehicle (UAV) platforms can be an agile and autonomous method for monitoring forest restoration projects, especially under conditions when information updates are frequently needed in relatively small areas or, when using an airplane-borne lidar system may be not financially viable. Here, we explored the potential of an UAV-borne lidar system to assess the outcomes of a mixed-species restoration plantation experiment, designed to maximize aboveground biomass (AGB) accumulation. The experiment was established in Brazil’s Atlantic Forest, with 20 native tree species, by combining two levels of planting density and two management levels, totaling four treatment combinations and one control (plots left over for natural regeneration). We analyzed three structural variables from lidar data (canopy height, gap fraction and leaf area index) and one from field inventory data (AGB). Structural differences between the treatments and the control plots were reliably distinguished by the UAV-borne lidar system. AGB was strongly correlated with canopy height, allowing us to elaborate a predictive equation to use the UAV-borne lidar system for monitoring structural features in other restoration plantations in the region. UAV-borne lidar systems showed enormous potential for monitoring relatively broad-scale (thousands of hectares) forest restoration projects, providing an important tool to aid decision making and accountability in forest landscape restoration.

Tropical forests are converted at an alarming rate for agricultural use and pastureland, but also regrow naturally through secondary succession. For successful forest restoration, it is essential to understand the mechanisms of secondary succession. These mechanisms may vary across forest types, but analyses across broad spatial scales are lacking. Here, we analyse forest recovery using 1,403 plots that differ in age since agricultural abandonment from 50 sites across the Neotropics. We analyse changes in community composition using species-specific stem wood density (WD), which is a key trait for plant growth, survival and forest carbon storage. In wet forest, succession proceeds from low towards high community WD (acquisitive towards conservative trait values), in line with standard successional theory. However, in dry forest, succession proceeds from high towards low community WD (conservative towards acquisitive trait values), probably because high WD reflects drought tolerance in harsh early successional environments. Dry season intensity drives WD recovery by influencing the start and trajectory of succession, resulting in convergence of the community WD over time as vegetation cover builds up. These ecological insights can be used to improve species selection for reforestation. Reforestation species selected to establish a first protective canopy layer should, among other criteria, ideally have a similar WD to the early successional communities that dominate under the prevailing macroclimatic conditions.

Canopy disturbance explains liana abundance and distribution within tropical forests and thus may also explain the widespread pattern of increasing liana abundance; however, this hypothesis remains untested. We used a 10-year study (2007-2017) of 117,100 rooted lianas in an old-growth Panamanian forest to test whether local canopy disturbance explains increasing liana abundance. We found that liana density increased 29.2% and basal area 12.5%. The vast majority of these increases were associated with clonal stem proliferation following canopy disturbance, particularly in liana-dense, low-canopy gaps, which had far greater liana increases than did undisturbed forest. Lianas may be ecological niche constructors, arresting tree regeneration in gaps and thus creating a high-light environment that favours sustained liana proliferation. Our findings demonstrate that liana abundance is increasing rapidly and their ability to proliferate via copious clonal stem production in canopy gaps explains much of their increase in this and possibly other tropical forests.

Agricultural land use leads to significant changes in both the quality (e.g., sources and compositions) and quantity of dissolved organic matter (DOM) exported from terrestrial to aquatic ecosystems. However, the effect of agricultural activities often interacts with those of hydroclimatic drivers, making it difficult to delineate agriculture-induced changes and identify associated mechanisms. Using partial least square path modeling (PLS-PM), we examined the relative importance of agricultural land use, stream order, precipitation, and temperature in mediating allochthonous versus autochthonous sources and pathways that influenced stream DOM quality and quantity. We analyzed stream water DOM from 15 small streams draining watersheds across a gradient of agricultural land use in Southeast USA for about one year. For DOM quantity, agricultural land use increased the export of DOC and various DOM pools (terrestrial humic, microbial humic, and protein-like DOM) from land to streams, and for DOM quality, agricultural streams showed greater proportions of microbial humic compounds than forested streams. The PLS-PM model for DOM quantity accounted for 75.5% of total variance and identified that agricultural land use increased stream water DOM quantity primarily through increasing allochthonous inputs, which can be attributed to shallower flow paths in agricultural watersheds that enabled the export of organic materials from the upper, organic-rich soil horizon. PLS-PM models for DOM quality only explained ~13% of the total variance, highlighting the complex dynamics between environmental drivers and stream water DOM. Relative to commonly used multivariate statistic modeling (e.g., redundancy analysis (RDA)), PLS-PM models offer the advantages of identifying the primary pathway by which agricultural lands alter freshwater DOM and quantifying the relative importance of interactive effects of agriculture and hydroclimatic drivers. Therefore, structural equation modeling is a powerful tool that should be more widely adopted to distinguish among multiple drivers and mechanisms regulating freshwater biogeochemistry.

Recently, remotely sensed measurements of the near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation have emerged as indicators of vegetation structure and function with potential to enhance or improve upon commonly used indicators, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The applicability of these remotely sensed indices to tropical forests, key ecosystems for global carbon cycling and biodiversity, has been limited. In particular, fine-scale spatial and temporal heterogeneity of structure and physiology may contribute to variation in these indices and the properties that are presumed to be tracked by them, such as gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR). In this study, fine-scale (approx. 15 cm) tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad and by lidar-derived height is investigated and compared to NIRv and EVI using unoccupied aerial system (UAS)-based hyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv, FCVI, and NIRvrad captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI) and then the normalized difference vegetation index (NDVI). Wavelet analyses showed the dominant spatial scale of variability of all indicators was driven by tree clusters and larger-than-tree-crown size gaps rather than individual tree crowns. NIRv, FCVI, NIRvrad, and EVI captured variability at smaller spatial scales (∼ 50 m) than NDVI (∼ 90 m) and the lidar-based surface model (∼ 70 m). We show that spatial and temporal patterns of NIRv and FCVI were virtually identical for a dense green canopy, confirming predictions in earlier studies. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated more strongly with GPP and PAR than did other indicators. NIRv, FCVI, and NIRvrad, which are related to canopy structure and the radiation regime of vegetation canopies, are promising tools to improve understanding of tropical forest canopy structure and function.

Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials.

Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the recent literature on UAVsSS, to analyze the current status of the technology. Primary UAVsSS applications were found to be in areas of post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, and desert greening. Nonetheless, low survival rates of the seeds, future forest diversity, weather limitations, financial constraints, and seed-firing accuracy concerns were determined as major challenges to operationalization. Based on our literature survey and qualitative analysis, twelve recommendations—ranging from the need for publishing germination results to linking UAVsSS operations with carbon offset markets—are provided for the advancement of UAVsSS applications.

In the pine savannas of the southeastern United States, prescribed fire is commonly used to manipulate understory structure and composition. Understory characteristics have traditionally been monitored with field sampling, however, remote sensing could provide rapid, spatially explicit monitoring of understory dynamics. We contrasted pre- vs. post-fire understory characteristics collected with fixed area plots with estimates from high-density LiDAR point clouds collected using the unmanned aerial vehicle (UAV)-borne GatorEye system. Measuring within 1 &times, 1 m field plots (n = 20), we found average understory height ranged from 0.17&ndash, 1.26 m and biomass from 0.26&ndash, 4.86 Mg C ha&minus, 1 before the fire (May 2018), and five months after the fire (November 2018), height ranged from 0.11&ndash, 1.09 m and biomass from 0.04&ndash, 3.03 Mg C ha&minus, 1. Understory heights estimated with LiDAR were significantly correlated with plot height measurements (R2 = 0.576, p &le, 0.001). Understory biomass was correlated with in situ heights (R2 = 0.579, p &le, 0.001) and LiDAR heights (R2 = 0.507, p &le, 0.001). The biomass estimates made with either height measurement did not differ for the measurement plots (p = 0.263). However, for the larger research area, the understory biomass estimated with the LiDAR indicated a smaller difference after the burn (~12.7% biomass reduction) than observed with in situ measurements (~16% biomass reduction). The two approaches likely differed because the research area&rsquo, s spatial variability was not captured by the in-situ measurements (0.2% of the research area measured) versus the wall-to-wall coverage provided by LiDAR. The additional benefit of having spatially explicit measurements with LiDAR, and its ease of use, make it a promising tool for land managers wanting greater spatial and temporal resolution in tracking understory biomass and its response to prescribed fire.

Trail detection in mixed canopy ecosystems has important implications for forest management, monitoring, and conservation, although active sensor technology for sub-canopy trail detection is still developing. In order to assess the effectiveness of UAV(Unmanned Aerial Vehicle)-borne lidar (light detection and ranging) data for small trails (< 2.5m width) in mixed forest canopy cover, we collected lidar data and trail characteristics (canopy cover and trail width) and created a high definition surface model map from the resulting lidar data, and also a high-resolution satellite imagery map using Google Earth. Through participatory mapping methods, seven respondents with limited prior geospatial experience completed a rapid identification of trails on both maps. Respondents’ trails were georeferenced in order to compare the rate of detectability between maps. We found greater detection on the lidar-derived map compared to the Google Earth map. Detectability in Google Earth maps was positively correlated with wider trails and trials with lower canopy. In lidar maps, trail detectability increased with wider trails, but canopy cover had no effect on detection rates. Our data indicate that a mixed-method approach that combines UAV-mounted lidar with high-resolution satellite imagery and participatory mapping increases rapid detection rates of small trails under varying canopy cover and trail widths.

Environmental Research Letters, 15 (12), ISSN:1748-9326, ISSN:1748-9318

Unmanned aerial vehicles (UAV) allow efficient acquisition of forest data at very high resolution at relatively low cost, making it useful for multi-temporal assessment of detailed tree crowns and forest structure. Single-pass flight plans provide rapid surveys for key selected high-priority areas, but their accuracy is still unexplored. We compared aircraft-borne LiDAR with GatorEye UAV-borne LiDAR in the Apalachicola National Forest, USA. The single-pass approach produced digital terrain models (DTMs), with less than 1 m differences compared to the aircraft-derived DTM within a 145° field of view (FOV). Canopy height models (CHM) provided reliable information from the top layer of the forest, allowing reliable treetop detection up to wide angles; however, underestimations of tree heights were detected at 175 m from the flightline, with an error of 2.57 ± 1.57. Crown segmentation was reliable only within a 60° FOV, from which the shadowing effect made it unviable. Reasonable quality threshold values for LiDAR products were: 195 m (145° FOV) for DTMs, 95 m (110° FOV) for CHM, 160 to 180 m (~140° FOV) for ITD and tree heights, and 40 to 60 m (~60° FOV) for crown delineation. These findings also support the definition of mission parameters for standard grid-based flight plans under similar forest types and flight parameters.

The palm oil industry is one of the major producers of vegetable oil in the tropics. Palm oil is used extensively for the manufacture of a wide variety of products and its production is increasing by around 9% every year, prompted largely by the expanding biofuel markets. The rise in annual demand for biofuels and vegetable oil from importer countries has caused a dramatic increase in the conversion of forests and peatlands into oil palm plantations in Malaysia. This study assessed the area of forests and peatlands converted into oil palm plantations from 1990 to 2018 in the states of Sarawak and Sabah, Malaysia, and estimated the resulting carbon dioxide (CO2) emissions. To do so, we analyzed multitemporal 30-m resolution Landsat-5 and Landsat-8 images using a hybrid method that combined automatic image processing and manual analyses. We found that over the 28-year period, forest cover declined by 12.6% and 16.3%, and the peatland area declined by 20.5% and 19.1% in Sarawak and Sabah, respectively. In 2018, we found that these changes resulted in CO2 emissions of 0.01577 and 0.00086 Gt CO2-C yr&minus, 1, as compared to an annual forest CO2 uptake of 0.26464 and 0.15007 Gt CO2-C yr&minus, 1, in Sarawak and Sabah, respectively. Our assessment highlights that carbon impacts extend beyond lost standing stocks, and result in substantial direct emissions from the oil palm plantations themselves, with 2018 oil palm plantations in our study area emitting up to 4% of CO2 uptake by remaining forests. Limiting future climate change impacts requires enhanced economic incentives for land uses that neither convert standing forests nor result in substantial CO2 emissions.

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 planning info:eu-repo/semantics/publishedVersion

Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( ryyˆ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( ryyˆ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (

Highlights • Pandemics can become a new indirect driver of tropical deforestation. • Halting illegal deforestation should be considered an essential activity during the pandemic. • Forest fires could aggravate the health risks of COVID-19. • Tropical deforestation will increase the risks of emerging zoonotic diseases. • Indigenous people should be especially protected during the current pandemic., Tropical deforestation drivers are complex and can change rapidly in periods of profound societal transformation, such as those during a pandemic. Evidence suggests that the COVID-19 pandemic has spurred illegal, opportunistic forest clearing in tropical countries, threatening forest ecosystems and their resident human communities. A total of 9,583 km2 of deforestation alerts from Global Land Analysis & Discovery (GLAD) were detected across the global tropics during the first month following the implementation of confinement measures of local governments to reduce COVID-19 spread, which is nearly double that of 2019 (4,732 km2). We present a conceptual framework linking tropical deforestation and the current pandemic. Zoonotic diseases, public health, economy, agriculture, and forests may all be reciprocally linked in complex positive and negative feedback loops with overarching consequences. We highlight the emerging threats to nature and society resulting from this complex reciprocal interplay and possible policy interventions that could minimize these threats.

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.

Quantifying fuel load over large areas is essential to support integrated fire management initiatives in fire-prone regions to preserve carbon stock, biodiversity and ecosystem functioning. It also allows a better understanding of global climate regulation as a potential carbon sink or source. Large area assessments usually require data from spaceborne remote sensors, but most of them cannot measure the vertical variability of vegetation structure, which is required for accurately measuring fuel loads and defining management interventions. The recently launched NASA's Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor holds potential to meet this demand. However, its capability for estimating fuel load has yet not been evaluated. In this study, we developed a novel framework and tested machine learning models for predicting multi-layer fuel load in the Brazilian tropical savanna (i.e., Cerrado biome) using GEDI data. First, lidar data were collected using an unnamed aerial vehicle (UAV). The flights were conducted over selected sample plots in distinct Cerrado vegetation formations (i.e., grassland, savanna, forest) where field measurements were conducted to determine the load of surface, herbaceous, shrubs and small trees, woody fuels and the total fuel load. Subsequently, GEDI-like full-waveforms were simulated from the high-density UAV-lidar 3-D point clouds from which vegetation structure metrics were calculated and correlated to field-derived fuel load components using Random Forest models. From these models, we generate fuel load maps for the entire Cerrado using all on-orbit available GEDI data. Overall, the models had better performance for woody fuels and total fuel loads (R2 = 0.88 and 0.71, respectively). For components at the lower stratum, models had moderate to low performance (R2 between 0.15 and 0.46) but still showed reliable results. The presented framework can be extended to other fire-prone regions where accurate measurements of fuel components are needed. We hope this study will contribute to the expansion of spaceborne lidar applications for integrated fire management activities and supporting carbon monitoring initiatives in tropical savannas worldwide.

Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary data for mapping targeted areas with high precision and systematic study of coupled natural and human systems. We report the results of data capture, analysis, and processing of UAV LiDAR data collected in the Maya Lowlands of Chiapas, Mexico in 2019 for a comparative landscape study. Six areas of archaeological settlement and long-term land-use reflecting a diversity of environments, land cover, and archaeological features were studied. These missions were characterized by areas that were variably forested, rugged, or flat, and included pre-Hispanic settlements and agrarian landscapes. Our study confirms that UAV LiDAR systems have great potential for broader application in high-precision archaeological mapping applications. We also conclude that these studies offer an important opportunity for multi-disciplinary collaboration. UAV LiDAR offers high-precision information that is not only useful for mapping archaeological features, but also provides critical information about long-term land use and landscape change in the context of archaeological resources.

International audience; Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data—canopy height, leaf area index (LAI), and understory LAI—and eighteen variables derived from hyperspectral data—15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale.

Urban forest remnants contribute to climate change mitigation by reducing the amount of carbon dioxide in urban areas. Hence, understanding the dynamics and the potential of urban forests as carbon pools is crucial to propose effective policies addressing the ecosystem services' maintenance. Remote sensing technologies such as Light detection and ranging (Lidar) are alternatives to acquire information on urban forests accurately. In this paper, we evaluate a UAV-Lidar system's potential to derive individual tree heights of Araucaria angustifolia trees in an Urban Atlantic Forest. Additionally, the influence of point density when deriving tree heights was assessed (2500, 1000, 500, 250, 100, 50, 25, 10 and 5 returns.m−2). The UAV-Lidar data was collected with the GatorEye Unmanned Flying Laboratory ‘Generation 2’. The UAV-Lidar-derived and field-based tree heights were compared by statistical analysis. Higher densities of points allowed a better description of tree profiles. Lower densities presented gaps in the Crown Height Model (CHM). The highest agreement between UAV-Lidar-derived and field-based tree heights (r = 0.73) was noticed when using 100 returns.m−2. The lowest rRMSE was observed for 50 returns.m−2 (8.35 %). There are no explicit differences in derived tree heights using 25 to 2500 returns.m−2. UAV-Lidar data presented satisfactory performance when deriving individual tree heights of Araucaria angustifolia trees.