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.

1.In forest ecosystems, many functional processes are governed by local canopy gap dynamics, caused by either natural or anthropogenic factors. Quantifying the size and spatial distribution of canopy gaps enables an improved understanding and predictive modeling of multiple environmental phenomena. For instance, knowledge of canopy gap dynamics can help us elucidate time‐integrated effects of tree mortality, regrowth and succession rates, carbon flux patterns, species heterogeneity, and three‐dimensional spacing within structurally complex forest ecosystems. 2.Airborne Laser Scanning (ALS) has emerged as a technology that is well‐suited for mapping forest canopy gaps in a wide variety of forest ecosystems and across spatial scales. New technological and algorithmic advances, including ALS remote‐sensing, coupled with optimized frameworks for data processing and detection of forest canopy gaps, are allowing an enhanced understanding of forest structure and functional processes. 3.This paper introduces ForestGapR, a cutting‐edge open source R package for forest gap analysis from canopy height models derived from ALS and other remote sensing sources. The ForestGapR package offers tools to i) automate forest canopy gap detection, ii) compute a series of gap statistics, including gap‐size frequency distributions and spatial distribution, iii) map gap dynamics (when multi‐temporal ALS data are available), and iv) convert forest canopy gaps detected into raster or vector layers as per user requirements. 4.As case studies, we run ForestGapR on ALS data collected over four different tropical forest regions worldwide. We hope this new package will enable further research towards understanding the distribution, dynamics, and role of canopy gaps not only in tropical forests, but in other forest types elsewhere.

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.

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.

The development of sustainable energy systems is very important to addressing the economic, environmental, and social pressures of the energy sector. Globally, buildings consume up to 40% of the world’s total energy. By 2030, it is expected to increase to 50%. Therefore, the world is facing a great challenge to overcome these problems related to global energy production. Malaysia is one of the top consumers of primary energy in Asia. In 2018, primary energy consumption for Malaysia was 3.79 quadrillion btu at an average annual rate of 4.58%. In this paper, we have carried out a detailed literature review on several previous studies of energy consumption in the world, especially in Malaysia, and how geographical information system (GIS) methods have been used for the spatial assessment of energy efficiency. Indeed, strategies of energy efficiency are essential in energy policy that could be created using various approaches used for energy savings in buildings. The findings of this review reveal that, for estimating energy consumption, exploring renewable energy sources, and investigating solar radiation, several geographic information system techniques such as multiple criteria decision analysis (MCDA), machine learning (ML), and deep learning (DL) are mainly utilized. The result indicates that the fuzzy DS method can more reliably determine the optimal PV farm locations. The 3D models are also regarded as an effective tool for estimating solar radiation, since this method generates a 3D model exportable to software tools. In addition, GIS and 3D can contribute to several purposes, such as sunlight access to buildings in urban areas, city growth prediction models and analysis of the habitability of public places.

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.