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  • 1
    Online Resource
    Online Resource
    Elsevier BV ; 2021
    In:  Current Opinion in Environmental Science & Health Vol. 23 ( 2021-10), p. 100291-
    In: Current Opinion in Environmental Science & Health, Elsevier BV, Vol. 23 ( 2021-10), p. 100291-
    Type of Medium: Online Resource
    ISSN: 2468-5844
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2915574-5
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  • 2
    In: Forest Ecology and Management, Elsevier BV, Vol. 382 ( 2016-12), p. 184-192
    Type of Medium: Online Resource
    ISSN: 0378-1127
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 2016648-5
    detail.hit.zdb_id: 751138-3
    SSG: 23
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    IOP Publishing ; 2021
    In:  Environmental Research Letters Vol. 16, No. 4 ( 2021-04-01), p. 044050-
    In: Environmental Research Letters, IOP Publishing, Vol. 16, No. 4 ( 2021-04-01), p. 044050-
    Abstract: Climate teleconnections (CT) synchronize and influence weather features such as temperature, precipitation and, subsequently, drought and fuel moisture in many regions across the globe. CTs thus may be related to cycles in wildfire activity, and thereby help fire managers to anticipate fire-prone weather conditions as well as envisaging their future evolution. A wide number of CTs modulate weather in the Iberian Peninsula (IP), exerting different levels of influence at different spatial and seasonal scales on a wide range of weather factors. In this work, we investigated the link between the most relevant CT patterns in the IP and fire activity and danger, exploring different spatial and temporal scales of aggregation. We analyzed a period of 36 years (1980–2015) using historical records of fire events ( 〉 100 ha burned) and the Canadian Fire Weather Index (FWI). Cross-correlation analysis was performed on monthly time series of CTs and fire data. Results pointed towards the North Atlantic Oscillation (in the western half of the IP) and Mediterranean Oscillation Index (along the Mediterranean coast) as the key CTs boosting burned area (BA) and fire weather danger in the IP. Both CTs relate to the relative position of the Azorean anticlone, fostering hazardous fire weather conditions during their positive phases, i.e. low rainfall and warm temperature leading to low fuel moisture content. The Scandinavian pattern index also played an important role in the western half of the Peninsula, linked to a decrease in rainfall during its negative phases. Nonetheless, the association between the CTs and BA (up to 0.5 Pearson’s R p 〈 0.05) was weaker than the observed between CTs and FWI (up to 0.75 Pearson’s R p 〈 0.05).
    Type of Medium: Online Resource
    ISSN: 1748-9326
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2255379-4
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  • 4
    In: Forests, MDPI AG, Vol. 9, No. 12 ( 2018-12-05), p. 759-
    Abstract: Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 〉 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2527081-3
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  Forests Vol. 8, No. 10 ( 2017-10-23), p. 402-
    In: Forests, MDPI AG, Vol. 8, No. 10 ( 2017-10-23), p. 402-
    Type of Medium: Online Resource
    ISSN: 1999-4907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2527081-3
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  • 6
    In: Forests, MDPI AG, Vol. 11, No. 12 ( 2020-11-29), p. 1285-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527081-3
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  • 7
    In: Remote Sensing, MDPI AG, Vol. 14, No. 13 ( 2022-07-01), p. 3172-
    Abstract: Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 8
    In: Remote Sensing, MDPI AG, Vol. 12, No. 5 ( 2020-03-07), p. 863-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 23 ( 2021-12-02), p. 4889-
    In: Remote Sensing, MDPI AG, Vol. 13, No. 23 ( 2021-12-02), p. 4889-
    Abstract: Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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  • 10
    In: Carbon Balance and Management, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2017-12)
    Type of Medium: Online Resource
    ISSN: 1750-0680
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2243512-8
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