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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.
Midhun Mohan; Gabriella Richardson; Gopika Gopan; Matthew Aghai; Shaurya Bajaj; G. Galgamuwa; Mikko Vastaranta; Pavithra Arachchige; Lot Amorós; Ana Corte; Sergio De-Miguel; Rodrigo Leite; Mahlatse Kganyago; Eben Broadbent; Willie Doaemo; Mohammed Shorab; Adrian Cardil. UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications. Remote Sensing 2021, 13, 2596 .
AMA StyleMidhun Mohan, Gabriella Richardson, Gopika Gopan, Matthew Aghai, Shaurya Bajaj, G. Galgamuwa, Mikko Vastaranta, Pavithra Arachchige, Lot Amorós, Ana Corte, Sergio De-Miguel, Rodrigo Leite, Mahlatse Kganyago, Eben Broadbent, Willie Doaemo, Mohammed Shorab, Adrian Cardil. UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications. Remote Sensing. 2021; 13 (13):2596.
Chicago/Turabian StyleMidhun Mohan; Gabriella Richardson; Gopika Gopan; Matthew Aghai; Shaurya Bajaj; G. Galgamuwa; Mikko Vastaranta; Pavithra Arachchige; Lot Amorós; Ana Corte; Sergio De-Miguel; Rodrigo Leite; Mahlatse Kganyago; Eben Broadbent; Willie Doaemo; Mohammed Shorab; Adrian Cardil. 2021. "UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications." Remote Sensing 13, no. 13: 2596.
Constructed landscapes are composed of diverse communities, representing different social strata and perspectives of a place. In turn, the risks associated with inhabiting unpredictable environments are disproportionately felt across urban and rural landscapes. The mitigation and management of risks often fall on farming and smallholder communities, influencing decentralized strategies. These themes are explored in an archaeological context surrounding the confluence of the Upper Usumacinta and Lacantún Rivers in the neotropical Maya lowlands of Chiapas, Mexico. LiDAR data collected recently with the GatorEye unoccupied aerial vehicle (UAV) and NASA’s GLiHT system have aided in the mapping of the archaeological urban centre of Benemérito de las Américas, Primera Sección and the surrounding landscape. These data have revealed coupled settlement with land management, in the form of wetland fields, reservoirs, and riverways, emphasizing the interconnectivity of household practice and land use in the region.
Whittaker Schroder; Timothy Murtha; Eben N. Broadbent; Angélica M. Almeyda Zambrano. A confluence of communities: households and land use at the junction of the Upper Usumacinta and Lacantún Rivers, Chiapas, Mexico. World Archaeology 2021, 1 -28.
AMA StyleWhittaker Schroder, Timothy Murtha, Eben N. Broadbent, Angélica M. Almeyda Zambrano. A confluence of communities: households and land use at the junction of the Upper Usumacinta and Lacantún Rivers, Chiapas, Mexico. World Archaeology. 2021; ():1-28.
Chicago/Turabian StyleWhittaker Schroder; Timothy Murtha; Eben N. Broadbent; Angélica M. Almeyda Zambrano. 2021. "A confluence of communities: households and land use at the junction of the Upper Usumacinta and Lacantún Rivers, Chiapas, Mexico." World Archaeology , no. : 1-28.
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.
Ernandes Macedo Da Cunha Neto; Franciel Eduardo Rex; Hudson Franklin Pessoa Veras; Marks Melo Moura; Carlos Roberto Sanquetta; Pâmela Suélen Käfer; Mateus Niroh Inoue Sanquetta; Angelica Maria Almeyda Zambrano; Eben North Broadbent; Ana Paula Dalla Corte. Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest. Urban Forestry & Urban Greening 2021, 63, 127197 .
AMA StyleErnandes Macedo Da Cunha Neto, Franciel Eduardo Rex, Hudson Franklin Pessoa Veras, Marks Melo Moura, Carlos Roberto Sanquetta, Pâmela Suélen Käfer, Mateus Niroh Inoue Sanquetta, Angelica Maria Almeyda Zambrano, Eben North Broadbent, Ana Paula Dalla Corte. Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest. Urban Forestry & Urban Greening. 2021; 63 ():127197.
Chicago/Turabian StyleErnandes Macedo Da Cunha Neto; Franciel Eduardo Rex; Hudson Franklin Pessoa Veras; Marks Melo Moura; Carlos Roberto Sanquetta; Pâmela Suélen Käfer; Mateus Niroh Inoue Sanquetta; Angelica Maria Almeyda Zambrano; Eben North Broadbent; Ana Paula Dalla Corte. 2021. "Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest." Urban Forestry & Urban Greening 63, no. : 127197.
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 estimating 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.
Máira Beatriz Teixeira da Costa; Carlos Alberto Silva; Eben North Broadbent; Rodrigo Vieira Leite; Midhun Mohan; Veraldo Liesenberg; Jaz Stoddart; Cibele Hummel Do Amaral; Danilo Roberti Alves de Almeida; Anne Laura da Silva; Lucas Ruggeri Ré Y. Goya; Victor Almeida Cordeiro; Franciel Rex; Andre Hirsch; Gustavo Eduardo Marcatti; Adrian Cardil; Bruno Araujo Furtado de Mendonça; Caio Hamamura; Ana Paula Dalla Corte; Eraldo Aparecido Trondoli Matricardi; Andrew T. Hudak; Angelica Maria Almeyda Zambrano; Ruben Valbuena; Bruno Lopes de Faria; Celso H.L. Silva Junior; Luiz Aragao; Manuel Eduardo Ferreira; Jingjing Liang; Samuel De Pádua Chaves E Carvalho; Carine Klauberg. Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data. Forest Ecology and Management 2021, 491, 119155 .
AMA StyleMáira Beatriz Teixeira da Costa, Carlos Alberto Silva, Eben North Broadbent, Rodrigo Vieira Leite, Midhun Mohan, Veraldo Liesenberg, Jaz Stoddart, Cibele Hummel Do Amaral, Danilo Roberti Alves de Almeida, Anne Laura da Silva, Lucas Ruggeri Ré Y. Goya, Victor Almeida Cordeiro, Franciel Rex, Andre Hirsch, Gustavo Eduardo Marcatti, Adrian Cardil, Bruno Araujo Furtado de Mendonça, Caio Hamamura, Ana Paula Dalla Corte, Eraldo Aparecido Trondoli Matricardi, Andrew T. Hudak, Angelica Maria Almeyda Zambrano, Ruben Valbuena, Bruno Lopes de Faria, Celso H.L. Silva Junior, Luiz Aragao, Manuel Eduardo Ferreira, Jingjing Liang, Samuel De Pádua Chaves E Carvalho, Carine Klauberg. Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data. Forest Ecology and Management. 2021; 491 ():119155.
Chicago/Turabian StyleMáira Beatriz Teixeira da Costa; Carlos Alberto Silva; Eben North Broadbent; Rodrigo Vieira Leite; Midhun Mohan; Veraldo Liesenberg; Jaz Stoddart; Cibele Hummel Do Amaral; Danilo Roberti Alves de Almeida; Anne Laura da Silva; Lucas Ruggeri Ré Y. Goya; Victor Almeida Cordeiro; Franciel Rex; Andre Hirsch; Gustavo Eduardo Marcatti; Adrian Cardil; Bruno Araujo Furtado de Mendonça; Caio Hamamura; Ana Paula Dalla Corte; Eraldo Aparecido Trondoli Matricardi; Andrew T. Hudak; Angelica Maria Almeyda Zambrano; Ruben Valbuena; Bruno Lopes de Faria; Celso H.L. Silva Junior; Luiz Aragao; Manuel Eduardo Ferreira; Jingjing Liang; Samuel De Pádua Chaves E Carvalho; Carine Klauberg. 2021. "Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data." Forest Ecology and Management 491, no. : 119155.
Afforestation/reforestation (A/R) programs spearheaded by Civil Society Organizations (CSOs) play a significant role in reaching global climate policy targets and helping low-income nations meet the United Nations (UN) Sustainable Development Goals (SDGs). However, these organizations face unprecedented challenges due to the COVID-19 pandemic. Consequently, these challenges affect their ability to address issues associated with deforestation and forest degradation in a timely manner. We discuss the influence COVID-19 can have on previous, present and future A/R initiatives, in particular, the ones led by International Non-governmental Organizations (INGOs). We provide thirty-three recommendations for exploring underlying deforestation patterns and optimizing forest policy reforms to support forest cover expansion during the pandemic. The recommendations are classified into four groups - i) curbing deforestation and improving A/R, ii) protecting the environment and mitigating climate change, iii) enhancing socio-economic conditions, and iv) amending policy and law enforcement practices.
Midhun Mohan; Hayden A. Rue; Shaurya Bajaj; G.A. Pabodha Galgamuwa; Esmaeel Adrah; Matthew Mehdi Aghai; Eben North Broadbent; Omkar Khadamkar; Sigit D. Sasmito; Joseph Roise; Willie Doaemo; Adrian Cardil. Afforestation, reforestation and new challenges from COVID-19: Thirty-three recommendations to support civil society organizations (CSOs). Journal of Environmental Management 2021, 287, 112277 .
AMA StyleMidhun Mohan, Hayden A. Rue, Shaurya Bajaj, G.A. Pabodha Galgamuwa, Esmaeel Adrah, Matthew Mehdi Aghai, Eben North Broadbent, Omkar Khadamkar, Sigit D. Sasmito, Joseph Roise, Willie Doaemo, Adrian Cardil. Afforestation, reforestation and new challenges from COVID-19: Thirty-three recommendations to support civil society organizations (CSOs). Journal of Environmental Management. 2021; 287 ():112277.
Chicago/Turabian StyleMidhun Mohan; Hayden A. Rue; Shaurya Bajaj; G.A. Pabodha Galgamuwa; Esmaeel Adrah; Matthew Mehdi Aghai; Eben North Broadbent; Omkar Khadamkar; Sigit D. Sasmito; Joseph Roise; Willie Doaemo; Adrian Cardil. 2021. "Afforestation, reforestation and new challenges from COVID-19: Thirty-three recommendations to support civil society organizations (CSOs)." Journal of Environmental Management 287, no. : 112277.
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 × 1 m field plots (n = 20), we found average understory height ranged from 0.17–1.26 m and biomass from 0.26–4.86 Mg C ha−1 before the fire (May 2018), and five months after the fire (November 2018), height ranged from 0.11–1.09 m and biomass from 0.04–3.03 Mg C ha−1. Understory heights estimated with LiDAR were significantly correlated with plot height measurements (R2 = 0.576, p ≤ 0.001). Understory biomass was correlated with in situ heights (R2 = 0.579, p ≤ 0.001) and LiDAR heights (R2 = 0.507, p ≤ 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’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.
Maryada Shrestha; Eben N. Broadbent; Jason G. Vogel. Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna. Forests 2020, 12, 38 .
AMA StyleMaryada Shrestha, Eben N. Broadbent, Jason G. Vogel. Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna. Forests. 2020; 12 (1):38.
Chicago/Turabian StyleMaryada Shrestha; Eben N. Broadbent; Jason G. Vogel. 2020. "Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna." Forests 12, no. 1: 38.
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.
Mabel Cesarina Báez; Angélica María Almeyda Zambrano; Beatriz Lopez Gutierrez; Gretchen Stokes; Jaime Chavez; Pamela Montero-Alvarez; Ana Oliveira Fiorini; Diego Garcia Olaechea; Ben Wilkinson; Eben North Broadbent. Comparing High-Resolution Satellite and GatorEye UAV Lidar Data for Trail Mapping in Mixed Pine and Oak Forests in Central Florida Using a Participatory Approach. 2020, 1 .
AMA StyleMabel Cesarina Báez, Angélica María Almeyda Zambrano, Beatriz Lopez Gutierrez, Gretchen Stokes, Jaime Chavez, Pamela Montero-Alvarez, Ana Oliveira Fiorini, Diego Garcia Olaechea, Ben Wilkinson, Eben North Broadbent. Comparing High-Resolution Satellite and GatorEye UAV Lidar Data for Trail Mapping in Mixed Pine and Oak Forests in Central Florida Using a Participatory Approach. . 2020; ():1.
Chicago/Turabian StyleMabel Cesarina Báez; Angélica María Almeyda Zambrano; Beatriz Lopez Gutierrez; Gretchen Stokes; Jaime Chavez; Pamela Montero-Alvarez; Ana Oliveira Fiorini; Diego Garcia Olaechea; Ben Wilkinson; Eben North Broadbent. 2020. "Comparing High-Resolution Satellite and GatorEye UAV Lidar Data for Trail Mapping in Mixed Pine and Oak Forests in Central Florida Using a Participatory Approach." , no. : 1.
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.
Gabriel Prata; Eben Broadbent; Danilo De Almeida; Joseph Peter; Jason Drake; Paul Medley; Ana Corte; Jason Vogel; Ajay Sharma; Carlos Silva; Angelica Zambrano; Ruben Valbuena; Ben Wilkinson. Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure. Remote Sensing 2020, 12, 4111 .
AMA StyleGabriel Prata, Eben Broadbent, Danilo De Almeida, Joseph Peter, Jason Drake, Paul Medley, Ana Corte, Jason Vogel, Ajay Sharma, Carlos Silva, Angelica Zambrano, Ruben Valbuena, Ben Wilkinson. Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure. Remote Sensing. 2020; 12 (24):4111.
Chicago/Turabian StyleGabriel Prata; Eben Broadbent; Danilo De Almeida; Joseph Peter; Jason Drake; Paul Medley; Ana Corte; Jason Vogel; Ajay Sharma; Carlos Silva; Angelica Zambrano; Ruben Valbuena; Ben Wilkinson. 2020. "Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure." Remote Sensing 12, no. 24: 4111.
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−1, as compared to an annual forest CO2 uptake of 0.26464 and 0.15007 Gt CO2-C yr−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.
Wan Shafrina Wan Mohd Jaafar; Nor Fitrah Syazwani Said; Khairul Nizam Abdul Maulud; Royston Uning; Mohd Talib Latif; Aisyah Marliza Muhmad Kamarulzaman; Midhun Mohan; Biswajeet Pradhan; Siti Nor Maizah Saad; Eben North Broadbent; Adrián Cardil; Carlos Alberto Silva; Mohd Sobri Takriff. Carbon Emissions from Oil Palm Induced Forest and Peatland Conversion in Sabah and Sarawak, Malaysia. Forests 2020, 11, 1285 .
AMA StyleWan Shafrina Wan Mohd Jaafar, Nor Fitrah Syazwani Said, Khairul Nizam Abdul Maulud, Royston Uning, Mohd Talib Latif, Aisyah Marliza Muhmad Kamarulzaman, Midhun Mohan, Biswajeet Pradhan, Siti Nor Maizah Saad, Eben North Broadbent, Adrián Cardil, Carlos Alberto Silva, Mohd Sobri Takriff. Carbon Emissions from Oil Palm Induced Forest and Peatland Conversion in Sabah and Sarawak, Malaysia. Forests. 2020; 11 (12):1285.
Chicago/Turabian StyleWan Shafrina Wan Mohd Jaafar; Nor Fitrah Syazwani Said; Khairul Nizam Abdul Maulud; Royston Uning; Mohd Talib Latif; Aisyah Marliza Muhmad Kamarulzaman; Midhun Mohan; Biswajeet Pradhan; Siti Nor Maizah Saad; Eben North Broadbent; Adrián Cardil; Carlos Alberto Silva; Mohd Sobri Takriff. 2020. "Carbon Emissions from Oil Palm Induced Forest and Peatland Conversion in Sabah and Sarawak, Malaysia." Forests 11, no. 12: 1285.
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 efficiency 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-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different 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 effects. 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 coefficient (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 effect variable, and canopy height, crown volume, and crown projected area as fixed effects. 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.
Rodrigo Leite; Carlos Silva; Midhun Mohan; Adrián Cardil; Danilo Almeida; Samuel Carvalho; Wan Jaafar; Juan Guerra-Hernández; Aaron Weiskittel; Andrew Hudak; Eben Broadbent; Gabriel Prata; Ruben Valbuena; Hélio Leite; Mariana Taquetti; Alvaro Soares; Henrique Scolforo; Cibele Amaral; Ana Dalla Corte; Carine Klauberg. Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models. Remote Sensing 2020, 12, 3599 .
AMA StyleRodrigo Leite, Carlos Silva, Midhun Mohan, Adrián Cardil, Danilo Almeida, Samuel Carvalho, Wan Jaafar, Juan Guerra-Hernández, Aaron Weiskittel, Andrew Hudak, Eben Broadbent, Gabriel Prata, Ruben Valbuena, Hélio Leite, Mariana Taquetti, Alvaro Soares, Henrique Scolforo, Cibele Amaral, Ana Dalla Corte, Carine Klauberg. Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models. Remote Sensing. 2020; 12 (21):3599.
Chicago/Turabian StyleRodrigo Leite; Carlos Silva; Midhun Mohan; Adrián Cardil; Danilo Almeida; Samuel Carvalho; Wan Jaafar; Juan Guerra-Hernández; Aaron Weiskittel; Andrew Hudak; Eben Broadbent; Gabriel Prata; Ruben Valbuena; Hélio Leite; Mariana Taquetti; Alvaro Soares; Henrique Scolforo; Cibele Amaral; Ana Dalla Corte; Carine Klauberg. 2020. "Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models." Remote Sensing 12, no. 21: 3599.
The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches - namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting - on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (field-derived) such as diameter at breast height, total height, and timber volume. A total of 370 trees had their dbh and height measured for validation purposes. Using LAStools we generated normalized Light Detection and Ranging (Lidar) point clouds and created a raster canopy height model at a 0.5x0.5 m spatial resolution following the construction of a digital terrain model and a digital surface model. The R package ‘lidR’ was set with the functions tree_detection (local maximum filter algorithm) and lastrees. Subsequently, we applied the function tree_metrics to extract individual metrics. Machine learning techniques were applied to the derived metrics to estimate dendrometric field measures. The machine learning models (MLM) with optimal hyperparameters showed similar predictive performances for modeling the variables diameter, height, and volume. All models had a rRMSE below 15% (for diameter at breast height), 9% (for height) and 29% (for volume). The Support Vector Regression algorithm showed the best performance. Our work demonstrates that all tested machine learning models are adequate and robust to handle the high dimensionality of UAV-Lidar data for the estimation of individual attributes, with Support Vector Regression model being the best performer in terms of minimal error rates.
Ana Paula Dalla Corte; Deivison Venicio Souza; Franciel Eduardo Rex; Carlos Roberto Sanquetta; Midhun Mohan; Carlos Alberto Silva; Angelica Maria Almeyda Zambrano; Gabriel Prata; Danilo Roberti Alves de Almeida; Jonathan William Trautenmüller; Carine Klauberg; Anibal de Moraes; Mateus N. Sanquetta; Ben Wilkinson; Eben North Broadbent. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture 2020, 179, 105815 .
AMA StyleAna Paula Dalla Corte, Deivison Venicio Souza, Franciel Eduardo Rex, Carlos Roberto Sanquetta, Midhun Mohan, Carlos Alberto Silva, Angelica Maria Almeyda Zambrano, Gabriel Prata, Danilo Roberti Alves de Almeida, Jonathan William Trautenmüller, Carine Klauberg, Anibal de Moraes, Mateus N. Sanquetta, Ben Wilkinson, Eben North Broadbent. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture. 2020; 179 ():105815.
Chicago/Turabian StyleAna Paula Dalla Corte; Deivison Venicio Souza; Franciel Eduardo Rex; Carlos Roberto Sanquetta; Midhun Mohan; Carlos Alberto Silva; Angelica Maria Almeyda Zambrano; Gabriel Prata; Danilo Roberti Alves de Almeida; Jonathan William Trautenmüller; Carine Klauberg; Anibal de Moraes; Mateus N. Sanquetta; Ben Wilkinson; Eben North Broadbent. 2020. "Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes." Computers and Electronics in Agriculture 179, no. : 105815.
Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future.
Danielle L. Lacouture; Eben N. Broadbent; Raelene M. Crandall. Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI). Forests 2020, 11, 749 .
AMA StyleDanielle L. Lacouture, Eben N. Broadbent, Raelene M. Crandall. Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI). Forests. 2020; 11 (7):749.
Chicago/Turabian StyleDanielle L. Lacouture; Eben N. Broadbent; Raelene M. Crandall. 2020. "Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI)." Forests 11, no. 7: 749.
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult 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-efficient 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 differences 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). Differences 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.
Marcus D’Oliveira; Eben Broadbent; Luis Oliveira; Danilo Almeida; Daniel Papa; Manuel Ferreira; Angelica Zambrano; Carlos Silva; Felipe Avino; Gabriel Prata; Ricardo Mello; Evandro Figueiredo; Lúcio Jorge; Leomar Junior; Rafael Albuquerque; Pedro Brancalion; Ben Wilkinson; Marcelo Oliveira-Da-Costa. Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil. Remote Sensing 2020, 12, 1754 .
AMA StyleMarcus D’Oliveira, Eben Broadbent, Luis Oliveira, Danilo Almeida, Daniel Papa, Manuel Ferreira, Angelica Zambrano, Carlos Silva, Felipe Avino, Gabriel Prata, Ricardo Mello, Evandro Figueiredo, Lúcio Jorge, Leomar Junior, Rafael Albuquerque, Pedro Brancalion, Ben Wilkinson, Marcelo Oliveira-Da-Costa. Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil. Remote Sensing. 2020; 12 (11):1754.
Chicago/Turabian StyleMarcus D’Oliveira; Eben Broadbent; Luis Oliveira; Danilo Almeida; Daniel Papa; Manuel Ferreira; Angelica Zambrano; Carlos Silva; Felipe Avino; Gabriel Prata; Ricardo Mello; Evandro Figueiredo; Lúcio Jorge; Leomar Junior; Rafael Albuquerque; Pedro Brancalion; Ben Wilkinson; Marcelo Oliveira-Da-Costa. 2020. "Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil." Remote Sensing 12, no. 11: 1754.
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 ( r y y ^ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( r y y ^ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (
Rodrigo Vieira Leite; Cibele Hummel Do Amaral; Raul De Paula Pires; Carlos Alberto Silva; Carlos Pedro Boechat Soares; Renata Paulo Macedo; Antonilmar Araújo Lopes Da Silva; Eben North Broadbent; Midhun Mohan; Hélio Garcia Leite. Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches. Remote Sensing 2020, 12, 1513 .
AMA StyleRodrigo Vieira Leite, Cibele Hummel Do Amaral, Raul De Paula Pires, Carlos Alberto Silva, Carlos Pedro Boechat Soares, Renata Paulo Macedo, Antonilmar Araújo Lopes Da Silva, Eben North Broadbent, Midhun Mohan, Hélio Garcia Leite. Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches. Remote Sensing. 2020; 12 (9):1513.
Chicago/Turabian StyleRodrigo Vieira Leite; Cibele Hummel Do Amaral; Raul De Paula Pires; Carlos Alberto Silva; Carlos Pedro Boechat Soares; Renata Paulo Macedo; Antonilmar Araújo Lopes Da Silva; Eben North Broadbent; Midhun Mohan; Hélio Garcia Leite. 2020. "Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches." Remote Sensing 12, no. 9: 1513.
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.
Franciel Eduardo Rex; Carlos Alberto Silva; Ana Paula Dalla Corte; Carine Klauberg; Midhun Mohan; Adrián Cardil; Vanessa Sousa Da Silva; Danilo Roberti Alves De Almeida; Mariano Garcia; Eben North Broadbent; Ruben Valbuena; Jaz Stoddart; Trina Merrick; Andrew Thomas Hudak. Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sensing 2020, 12, 1498 .
AMA StyleFranciel Eduardo Rex, Carlos Alberto Silva, Ana Paula Dalla Corte, Carine Klauberg, Midhun Mohan, Adrián Cardil, Vanessa Sousa Da Silva, Danilo Roberti Alves De Almeida, Mariano Garcia, Eben North Broadbent, Ruben Valbuena, Jaz Stoddart, Trina Merrick, Andrew Thomas Hudak. Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sensing. 2020; 12 (9):1498.
Chicago/Turabian StyleFranciel Eduardo Rex; Carlos Alberto Silva; Ana Paula Dalla Corte; Carine Klauberg; Midhun Mohan; Adrián Cardil; Vanessa Sousa Da Silva; Danilo Roberti Alves De Almeida; Mariano Garcia; Eben North Broadbent; Ruben Valbuena; Jaz Stoddart; Trina Merrick; Andrew Thomas Hudak. 2020. "Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data." Remote Sensing 12, no. 9: 1498.
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.
Vanessa Sousa Da Silva; Carlos Alberto Silva; Midhun Mohan; Adrián Cardil; Franciel Eduardo Rex; Gabrielle Hambrecht Loureiro; Danilo Roberti Alves De Almeida; Eben North Broadbent; Eric Bastos Gorgens; Ana Paula Dalla Corte; Emanuel Araújo Silva; Rubén Valbuena; Carine Klauberg. Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data. Remote Sensing 2020, 12, 1438 .
AMA StyleVanessa Sousa Da Silva, Carlos Alberto Silva, Midhun Mohan, Adrián Cardil, Franciel Eduardo Rex, Gabrielle Hambrecht Loureiro, Danilo Roberti Alves De Almeida, Eben North Broadbent, Eric Bastos Gorgens, Ana Paula Dalla Corte, Emanuel Araújo Silva, Rubén Valbuena, Carine Klauberg. Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data. Remote Sensing. 2020; 12 (9):1438.
Chicago/Turabian StyleVanessa Sousa Da Silva; Carlos Alberto Silva; Midhun Mohan; Adrián Cardil; Franciel Eduardo Rex; Gabrielle Hambrecht Loureiro; Danilo Roberti Alves De Almeida; Eben North Broadbent; Eric Bastos Gorgens; Ana Paula Dalla Corte; Emanuel Araújo Silva; Rubén Valbuena; Carine Klauberg. 2020. "Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data." Remote Sensing 12, no. 9: 1438.
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.
Ana Paula Dalla Corte; Franciel Eduardo Rex; Danilo Roberti Alves De Almeida; Carlos Roberto Sanquetta; Carlos A. Silva; Marks M. Moura; Ben Wilkinson; Angelica Maria Almeyda Zambrano; Ernandes M. Da Cunha Neto; Hudson F. P. Veras; Anibal De Moraes; Carine Klauberg; Midhun Mohan; Adrián Cardil; Eben North Broadbent. Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System. Remote Sensing 2020, 12, 863 .
AMA StyleAna Paula Dalla Corte, Franciel Eduardo Rex, Danilo Roberti Alves De Almeida, Carlos Roberto Sanquetta, Carlos A. Silva, Marks M. Moura, Ben Wilkinson, Angelica Maria Almeyda Zambrano, Ernandes M. Da Cunha Neto, Hudson F. P. Veras, Anibal De Moraes, Carine Klauberg, Midhun Mohan, Adrián Cardil, Eben North Broadbent. Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System. Remote Sensing. 2020; 12 (5):863.
Chicago/Turabian StyleAna Paula Dalla Corte; Franciel Eduardo Rex; Danilo Roberti Alves De Almeida; Carlos Roberto Sanquetta; Carlos A. Silva; Marks M. Moura; Ben Wilkinson; Angelica Maria Almeyda Zambrano; Ernandes M. Da Cunha Neto; Hudson F. P. Veras; Anibal De Moraes; Carine Klauberg; Midhun Mohan; Adrián Cardil; Eben North Broadbent. 2020. "Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System." Remote Sensing 12, no. 5: 863.
Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to
Benjamin Wilkinson; H. Andrew Lassiter; Amr Abd-Elrahman; Raymond R. Carthy; Peter Ifju; Eben Broadbent; Nathan Grimes. Geometric Targets for UAS Lidar. Remote Sensing 2019, 11, 3019 .
AMA StyleBenjamin Wilkinson, H. Andrew Lassiter, Amr Abd-Elrahman, Raymond R. Carthy, Peter Ifju, Eben Broadbent, Nathan Grimes. Geometric Targets for UAS Lidar. Remote Sensing. 2019; 11 (24):3019.
Chicago/Turabian StyleBenjamin Wilkinson; H. Andrew Lassiter; Amr Abd-Elrahman; Raymond R. Carthy; Peter Ifju; Eben Broadbent; Nathan Grimes. 2019. "Geometric Targets for UAS Lidar." Remote Sensing 11, no. 24: 3019.
Habitat loss is one of the main threats to wildlife. Therefore, knowledge of habitat use and preference is essential for the design of conservation strategies and identification of priority sites for the protection of endangered species. The yellow-tailed woolly monkey (Lagothrix flavicauda Humboldt, 1812), categorized as Critically Endangered on the IUCN Red List, is endemic to montane forests in northern Peru where its habitat is greatly threatened. We assessed how habitat use and preference in L. flavicauda are linked to forest structure and composition. The study took place near La Esperanza, in the Amazonas region, Peru. Our objective was to identify characteristics of habitat most utilized by L. flavicauda to provide information that will be useful for the selection of priority sites for conservation measures. Using presence records collected from May 2013 to February 2014 for one group of L. flavicauda, we classified the study site into three different use zones: low-use, medium-use, and high-use. We assessed forest structure and composition for all use zones using 0.1 ha Gentry vegetation transects. Results show high levels of variation in plant species composition across the three use zones. Plants used as food resources had considerably greater density, dominance, and ecological importance in high-use zones. High-use zones presented similar structure to medium- and low-use zones; thus it remains difficult to assess the influence of forest structure on habitat preference. We recommend focusing conservation efforts on areas with a similar floristic composition to the high-use zones recorded in this study and suggest utilizing key alimentation species for reforestation efforts.
Sandra L. Almeyda Zambrano; Eben N. Broadbent; Sam Shanee; Noga Shanee; Anneke DeLuycker; Michael Steinberg; Scott A. Ford; Alma Hernández Jaramillo; Robin Fernandez Hilario; Carolina Lagos Castillo; Angelica M. Almeyda Zambrano. Habitat preference in the critically endangered yellow‐tailed woolly monkey (Lagothrix flavicauda) at La Esperanza, Peru. American Journal of Primatology 2019, 81, e23032 .
AMA StyleSandra L. Almeyda Zambrano, Eben N. Broadbent, Sam Shanee, Noga Shanee, Anneke DeLuycker, Michael Steinberg, Scott A. Ford, Alma Hernández Jaramillo, Robin Fernandez Hilario, Carolina Lagos Castillo, Angelica M. Almeyda Zambrano. Habitat preference in the critically endangered yellow‐tailed woolly monkey (Lagothrix flavicauda) at La Esperanza, Peru. American Journal of Primatology. 2019; 81 (8):e23032.
Chicago/Turabian StyleSandra L. Almeyda Zambrano; Eben N. Broadbent; Sam Shanee; Noga Shanee; Anneke DeLuycker; Michael Steinberg; Scott A. Ford; Alma Hernández Jaramillo; Robin Fernandez Hilario; Carolina Lagos Castillo; Angelica M. Almeyda Zambrano. 2019. "Habitat preference in the critically endangered yellow‐tailed woolly monkey (Lagothrix flavicauda) at La Esperanza, Peru." American Journal of Primatology 81, no. 8: e23032.
Over 140 Mha of restoration commitments have been pledged across the global tropics, yet guidance is needed to identify those landscapes where implementation is likely to provide the greatest potential benefits and cost-effective outcomes. By overlaying seven recent, peer-reviewed spatial datasets as proxies for socioenvironmental benefits and feasibility of restoration, we identified restoration opportunities (areas with higher potential return of benefits and feasibility) in lowland tropical rainforest landscapes. We found restoration opportunities throughout the tropics. Areas scoring in the top 10% (i.e., restoration hotspots) are located largely within conservation hotspots (88%) and in countries committed to the Bonn Challenge (73%), a global effort to restore 350 Mha by 2030. However, restoration hotspots represented only a small portion (19.1%) of the Key Biodiversity Area network. Concentrating restoration investments in landscapes with high benefits and feasibility would maximize the potential to mitigate anthropogenic impacts and improve human well-being.
Pedro H. S. Brancalion; Aidin Niamir; Eben Broadbent; Renato Crouzeilles; Felipe S. M. Barros; Angelica M. Almeyda Zambrano; Alessandro Baccini; James Aronson; Scott Goetz; J. Leighton Reid; Bernardo B. N. Strassburg; Sarah Wilson; Robin L. Chazdon. Global restoration opportunities in tropical rainforest landscapes. Science Advances 2019, 5, eaav3223 .
AMA StylePedro H. S. Brancalion, Aidin Niamir, Eben Broadbent, Renato Crouzeilles, Felipe S. M. Barros, Angelica M. Almeyda Zambrano, Alessandro Baccini, James Aronson, Scott Goetz, J. Leighton Reid, Bernardo B. N. Strassburg, Sarah Wilson, Robin L. Chazdon. Global restoration opportunities in tropical rainforest landscapes. Science Advances. 2019; 5 (7):eaav3223.
Chicago/Turabian StylePedro H. S. Brancalion; Aidin Niamir; Eben Broadbent; Renato Crouzeilles; Felipe S. M. Barros; Angelica M. Almeyda Zambrano; Alessandro Baccini; James Aronson; Scott Goetz; J. Leighton Reid; Bernardo B. N. Strassburg; Sarah Wilson; Robin L. Chazdon. 2019. "Global restoration opportunities in tropical rainforest landscapes." Science Advances 5, no. 7: eaav3223.