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Mangrove land use changes of varied intensities have long been a problem in tropical mangrove forests. This has resulted in various degrees of mangrove land use modification, which catch many interests in the region for research. The research provided here is a bibliometric analysis of scholarly articles published around the world in various publication document types on changes in land use of tropical mangrove forests based on remote sensing and Geographical Information System (GIS). Scientific data analysis was undertaken by using bibliometric approaches on 6,574 papers extracted from the Scopus databases between 2010 and 2020. The findings revealed that the number of publications continuously climbed from under 400 to an average of 50-60 per year till 2019. The data showed that the mangrove forest modifications study gained traction when the highest number of citations, 9,236 in 2015, were observed. We can also notice that the overall number of citations fluctuated a lot during the first five years (2010-2015) but increased from 2013 to 2015. The findings demonstrate how remote sensing satellites have aided vegetation and land study in recent years. The findings also revealed that the analysis tools of Land Use Change, Vegetation Index, Mangrove, Tropical Country, Remote Sensing, and Tropical contributed to scientific knowledge of current issues of mangrove land use change in the tropical region. The authors' keywords, Remote Sensing in particular, supplied roughly 43%, Normalized Difference Vegetation Index (13%), Vegetation Index (9%), and other keywords contributed less than 7%. The growth pattern of the keywords "MODIS" and "Landsat" implies that both will stay important over the next five years, according to an analysis of the type of satellite used in land use assessment. Meanwhile, papers pertaining to policy on land use change, food security, and forest resources were evaluated in order to highlight policy and academic research findings on the topics. The application of the Normalized Difference Vegetation Index, which is a very relevant tool that can be used in monitoring land use changes and assessing vegetation status because it is a desirable technique in measuring plant health and vigour, can help fill the research gaps presented in this study. This review can help with the development of better mangrove land use change approaches in tropical mangroves and around the world using satellite remote sensing and GIS.

Peatland forest fires are a serious threat to the biodiversity-ecosystem and to human health in South East Asia, especially in the dry season. Here, approximately 2.13 million hectares of peat swamp forests are found and made up of 6.5% of the total land area. However, monitoring and management of sustainable peat swamp forests can be improved, as degradation of the forest may happen. One of the main challenges in the forest management process is the lack of manpower to obtain in-situ samples for analysis and monitoring purposes. This paper proposes an Internet of Things (IoT)-based peatland forest management and monitoring system in Raja Musa Forest Reserve (RMFR), Selangor, Malaysia. Real-time peatland data such as ground water level (GWL), soil temperature, and soil humidity as well as local meteorological data such as rain precipitation, wind speed, ambient temperature, and ambient humidity are obtained from deployed IoT system on sites. The peatland data from January to March 2020 are recorded and analyzed.

Natural forest, oil palm and rubber plantations are economically and environmentally important for Peninsular Malaysia. The present study analysed four years of moderate-resolution imaging spectroradiometer (MODIS) surface reflectance data to develop spectral indices of vegetation, water availability and moisture stress for the study area. The indices – the Normalised Difference Vegetation Index, the Normalised Difference Water Index and the Moisture Stress Index – were applied to the three different habitats to monitor drought and develop a Malaysia Southwest Monsoon (M-SWM) classification. By integrating indicators of the Southwest Monsoon, the Standard Precipitation Index, mean precipitation and temperature and spectral indices correlation analysis, M-SWM classification showed greater sensitivity to drought conditions than any of the individual indicators alone. The results also found that July is the driest month; it was the only period classified as ‘Very Dry’ based on the M-SWM.

Tropical forests support core biological, hydrological and socioeconomic functions essential to life on earth. An assessment based on the Human Appropriation of Net Primary Production (HANPP) could help reduce exploitation of these forests, increasing their adaptive capacity and lessening their vulnerability to losses of Net Primary Productivity (NPP). Here we apply HANPP to the study area, based on Land Use Impact variability between the forest and contiguous roads and plantations by application of Geographical Information Systems of Protected Area Tools. We used the human activity index and biomass extraction from forest to study the effects of population pressure. The final land use impact map showed that the largest area of forest land (37 %) is now in urban and agricultural use, and that these areas are located within 0–3 km of the forest land. NPP with human intervention showed, total NPP of the forest decreased by 7.4 %, from 104.4 to 96.6 gCm−2 month−1. This study developed a new HANPP model and enhanced the usefulness of HANPP indicators by demonstrating the impact of human activity inside the forest. Because NPP changes most in higher–productivity areas, suitable policies should be enforced to avoid further human interference in the area.

In response to growing concerns over the burning of peat swamp forests, researchers have begun developing methods of mapping forest fire. Forest fire is one of the major causes of deforestation of tropical peat swamps in Malaysia. A way of identifying which peat swamp forest is vulnerable to forest fire is to develop a fuel type map to classify forest fire into different risk levels. In this study, remote sensing and geographical information system (GIS) techniques were integrated. Landsat Thematic Mapper (TM) image dated April 3rd, 1999, which corresponded to fire incident in this study area was used. The objective of this paper is to map fuel types in peat swamp forests. Results show that greenness and wetness components of Tasselled cap, used in classification, accurately captured greener and wetter area by combining supervised image and Tasselled cap image. The overall kappa statistics was 0.94 for combined supervised and Tasselled Cap classification. High values of kappa statistics for certain vegetation classes were due to the availability of representative pixels in the classes.

Forest fires are one of the major causes of the deforestation of tropical peat swamps in Malaysia. One way of trying to identify which peat swamp forests are vulnerable to forest fire is to develop a forest fire risk index. The objectives of this study were to develop both a fuel-type map and a forest fire hazard rating assessment for the peat swamp forests. The study was conducted in a peat swamp forest located at Batu Enam, Penor/Kuantan District of Pahang. This area suffered a severe forest fire on 12 March 1998 which degraded the peat swamp area. Digitally processed Landsat Thematic Mapper (TM) satellite image were integrated with geographic information layer of fuel type, roads and canal layer to derive a fire hazard rating map of the area. Using the superior spectral and temporal resolution of a Landsat TM, five fire hazard rating classifications were defined. A forest fire hazard rating map was produced showing that 49% of the area was 'low' fire hazard rating, 23% was 'high', 17% was 'moderate', 10% was 'extreme' and 1% was 'null'. Peat lands within 150 meters of roads and of a canal were identified as having an 'extreme' fire hazard rating. Both the fire hazard rating map and the forest fire hazard rating assessment can be used in future forest fire management planning.