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  • 1
    Online Resource
    Online Resource
    Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery
    MDPI AG ; 2022
    In:  Forests Vol. 13, No. 1 ( 2022-01-02), p. 48-
    Details
    In: Forests, MDPI AG, Vol. 13, No. 1 ( 2022-01-02), p. 48-
    Abstract: Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f13010048
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527081-3
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  • 2
    Online Resource
    Online Resource
    Research Opportunity on Fractional Cover of Forest: A Bibliometric Review
    MDPI AG ; 2022
    In:  Forests Vol. 13, No. 10 ( 2022-10-10), p. 1664-
    Details
    In: Forests, MDPI AG, Vol. 13, No. 10 ( 2022-10-10), p. 1664-
    Abstract: Forests are threatened globally by deforestation. Forest restoration at the landscape scale can reduce these threats. Ground-based and remote sensing inventories are needed to assess restoration success. Fractional canopy cover estimated from forest algorithms can be used to monitor forest loss, growth, and health via remote sensing. Various studies on the fractional cover of forest have been published. However, none has yet conducted a bibliometric analysis. Bibliometrics provide a detailed examination of a topic, pointing academics to new research possibilities. To the best of the authors’ knowledge, this is the first bibliometric study screening publications to assess the incidence of studies of the fractional cover of forests in Web of Science (WoS) and Scopus databases. This research analyses WoS and Scopus publications on the fractional cover of forest dating from 1984 to 2021. The current study uses the Bibliometrix R-package for citation metrics and analysis. The first paper on the fractional cover of forest was published in 1984 and annual publication numbers have risen since 2002. USA and China were the most active countries in the study of fractional cover of forests. A total of 955 documents from 69 countries with multiple languages were retrieved. Vegetation, forestry, and remote sensing were the most discussed topics. Findings suggest more studies on the fractional cover of forests algorithms should be conducted in tropical forest from developing countries.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f13101664
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527081-3
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  • 3
    Online Resource
    Online Resource
    Carbon Emissions from Oil Palm Induced Forest and Peatland Conversion in Sabah and Sarawak, Malaysia
    MDPI AG ; 2020
    In:  Forests Vol. 11, No. 12 ( 2020-11-29), p. 1285-
    Details
    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
    URL: Article
    DOI: 10.3390/f11121285
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527081-3
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  • 4
    Online Resource
    Online Resource
    Tree Stump Height Estimation Using Canopy Height Model at Tropical Forest in Ulu Jelai Forest Reserve, Pahang, Malaysia
    IOP Publishing ; 2020
    In:  IOP Conference Series: Earth and Environmental Science Vol. 540, No. 1 ( 2020-07-01), p. 012015-
    Details
    In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 540, No. 1 ( 2020-07-01), p. 012015-
    Abstract: Assessing tree biomass is essential for observing carbon stock and forest biodiversity which are an important indicator in climate change monitoring. The most accurate assessment involved ground data collection, including its data processing. In certain condition, it is extremely challenging, due to the difficulties of accessing dense forest and variation of terrain, tedious and time-consuming. Therefore, due to these limitations, remote sensing might become a better approach in measuring this information. The focus of this study is to estimate the tree stump height for biomass estimation after selective logging practices. In this study, we utilize remotely sensed canopy height model (CHM) derived from Unmanned Aerial Vehicle (UAV) to quantify tree stump height after felling logs at a local scale. This study aims to investigate the feasibility of utilizing UAV imagery to derive a canopy height model (CHM) for preparing parameters in assessing timber tree biomass. CHM is the reference surface to derive statistics that will be used to estimate the forest variables. Data was obtained through UAV which flown at the logging compartment in Ulu Jelai Forest Reserve, Pahang, Malaysia. The estimated stump height obtained from this technique was compared with a measured stump on the ground. Based on scatterplot regression, it showed a significant relationship with a strong coefficient, R 2 of 0.8368. At this stage of the study, the performance of the result was not assessed since it is an only preliminary result and the study only focused on producing CHM for stump height estimation using the UAV platform only.
    Type of Medium: Online Resource
    ISSN: 1755-1307 , 1755-1315
    URL: Article
    DOI: 10.1088/1755-1315/540/1/012015
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2434538-6
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  • 5
    Online Resource
    Online Resource
    UAV Implementations in Urban Planning and Related Sectors of Rapidly Developing Nations: A Review and Future Perspectives for Malaysia
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 11 ( 2023-05-30), p. 2845-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 11 ( 2023-05-30), p. 2845-
    Abstract: The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across multiple fields, including precision agriculture, forestry, construction, surveying, disaster response, security, and education. They offer advantages such as high-resolution imagery, accessibility, and operational safety. Varying policies and regulations concerning UAV usage across countries present challenges for commercial and research UAVs. In Malaysia, UAVs have become essential in addressing challenges associated with urbanization, including traffic congestion, urban sprawl, pollution, and inadequate social facilities. However, several obstacles need to be overcome before UAVs can be effectively deployed, including regulatory barriers, limited flight time and range, restricted awareness, lack of skilled personnel, and concerns regarding security and privacy. Successful implementation requires coordination among public bodies, industry stakeholders, and the public. Future research in Malaysia should prioritize 3D modeling and building identification, using the results of this study to propel advancements in other ASEAN countries.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15112845
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 6
    Online Resource
    Online Resource
    An object-based approach to detect tree stumps in a selective logging area using Unmanned Aerial Vehicle imagery
    Penerbit Universiti Kebangsaan Malaysia (UKM Press) ; 2021
    In:  Malaysian Journal of Society and Space Vol. 17, No. 4 ( 2021-11-30)
    Details
    In: Malaysian Journal of Society and Space, Penerbit Universiti Kebangsaan Malaysia (UKM Press), Vol. 17, No. 4 ( 2021-11-30)
    Type of Medium: Online Resource
    ISSN: 2682-7727
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.17576/geo-2021-1704
    DOI: 10.17576/geo
    DOI: 10.17576/geo-2021-1704-24
    Language: Unknown
    Publisher: Penerbit Universiti Kebangsaan Malaysia (UKM Press)
    Publication Date: 2021
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  • 7
    Online Resource
    Online Resource
    Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
    Walter de Gruyter GmbH ; 2021
    In:  Open Geosciences Vol. 13, No. 1 ( 2021-09-13), p. 1028-1039
    Details
    In: Open Geosciences, Walter de Gruyter GmbH, Vol. 13, No. 1 ( 2021-09-13), p. 1028-1039
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2391-5447
    URL: Article
    DOI: 10.1515/geo-2020-0290
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2799881-2
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  • 8
    Online Resource
    Online Resource
    Modeling Carbon Emissions of Post-Selective Logging in the Production Forests of Ulu Jelai, Pahang, Malaysia
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 4 ( 2023-02-12), p. 1016-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 4 ( 2023-02-12), p. 1016-
    Abstract: Harvested timber and constructed infrastructure over the logging area leave massive damage that contributes to the emission of anthropogenic gases into the atmosphere. Carbon emissions from tropical deforestation and forest degradation are the second largest source of anthropogenic emissions of greenhouse gases. Even though the emissions vary from region to region, a significant amount of carbon emissions comes mostly from timber harvesting, which is tightly linked to the selective logging intensity. This study intended to utilize a remote sensing approach to quantify carbon emissions from selective logging activities in Ulu Jelai Forest Reserve, Pahang, Malaysia. To quantify the emissions, the relevant variables from the logging’s impact were identified as a predictor in the model development and were listed as stump height, stump diameter, cross-sectional area, timber volume, logging gaps, road, skid trails, and incidental damage resulting from the logging process. The predictive performance of linear regression and machine learning models, namely support vector machine (SVM), random forest, and K-nearest neighbor, were examined to assess the carbon emission from this degraded forest. To test the different methods, a combination of ground inventory plots, unmanned aerial vehicles (UAV), and satellite imagery were analyzed, and the performance in terms of root mean square error (RMSE), bias, and coefficient of correlation (R2) were calculated. Among the four models tested, the machine learning model SVM provided the best accuracy with an RMSE of 21.10% and a bias of 0.23% with an adjusted R2 of 0.80. Meanwhile, the linear model performed second with an RMSE of 22.14%, a bias of 0.72%, and an adjusted R2 of 0.75. This study demonstrates the efficacy of remotely sensed data to facilitate the conventional methods of quantifying carbon emissions from selective logging and promoting advanced assessments that are more effective, especially in massive logging areas and various forest conditions. Findings from this research will be useful in assisting the relevant authorities in optimizing logging practices to sustain forest carbon sequestration for climate change mitigation.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15041016
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 9
    Online Resource
    Online Resource
    The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia
    MDPI AG ; 2020
    In:  Forests Vol. 11, No. 6 ( 2020-06-11), p. 670-
    Details
    In: Forests, MDPI AG, Vol. 11, No. 6 ( 2020-06-11), p. 670-
    Abstract: Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f11060670
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527081-3
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