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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.

Knowledge of soil physical and chemical properties is vital to the optimal growing performance of agricultural crops, including plantation forest trees. Near infrared (NIR) spectroscopy has been shown to be a tool that enables rapid and low-cost assessment of soils, however its use in forest plantations has been slow to develop. This study shows the development of calibrations for total organic carbon, total nitrogen and soil pH using a handheld NIR spectrometer for soils at three sites in Sabah, Malaysia. Soil samples were collected, dried, milled and scanned after which they were analysed using standard chemical methods to obtain total organic carbon (TOC) and total nitrogen (TN). Partial least squares regression was used to develop calibrations between reference data and NIR spectra and validated using an independent sample set. The calibration of soil pH is made using a subset of samples across A- and B-horizons for samples from two of the three sites. The most effective spectral pre-treatment was the standard normal variate for TOC and TN while the Savitzky-Golay first derivative was the best pre-treatment for predicting soil pH. Principal component analysis was performed on the raw NIR spectra of all samples to confirm that the samples from different sites were able to be used in a single regression analysis. Kennard-Stone selection was used to create calibration sets and validation sets from the combined spectra from all sites and both soil horizons. Calibrations were also developed independently on the A- and B-horizon samples, but there were insufficient sample numbers to utilize an independent validation set. The coefficients of determination for the validation set (r2p) were 0.77 and 53 for TOC and TN respectively while the root mean square error of prediction (RMSEP) was 0.44 g 100 g−1for TOC and 0.051 g 100 g−1for TN. In addition, it showcases the application of these calibrations to provide spatial assessment of two differing micro-sites within a single Eucalyptus pellita progeny breeding trial. Combined with the potential to monitor foliar nutrients, the ability to obtain high spatial details of soil composition will assist tree plantation growers and also other agricultural producers, such as oil palm plantation managers, to better manage their soil and fertiliser regimes.

This study aimed to access different desirable characteristics of nine Hibiscus cannabinus L. accessions based on morphophysiological characteristics and fiber tensile strength for an effective selection of H. cannabinus plant improvement. Four China accessions (FH952, T15, T17, and T19), four Bangladesh accessions (HC2, HC95, V4202, and V4383), and V36 (control) accession were examined in a four-month cultivation period. The experimental design was arranged using randomized complete block design with three replications. Stem diameter was found to be significantly related (p ≤ 0.05) with all morphological and yield characteristics except for leaf dry weight and growth efficiency. Bigger stem diameter was an indicator of fiber yield in attempts to apply crossing and selection to improve performance. Photosynthesis rate also was found to be significantly related (p ≤ 0.05) with stomatal conductance, transpiration rate, instantaneous water use efficiency, and carboxylation efficiency. High photosynthesis rate could be an indicator to interpret the pattern of genetic variation of plant assimilation rate and its relation with environmental and agronomic factors. The fiber tensile modulus, however, was found to be inversely correlated with fiber diameter. The present study suggests the selection of control, V4383, HC2, and FH952 accessions for a breeding line as they possess high fiber yield, fiber strength, and photosynthetic efficiency.

Forests and agricultural plantations are vegetated areas that play an important role in catering to human needs. The characteristics of the two lands can be recognized by utilizing remote sensing techniques of WorldView-2 satellite images as green biomass monitoring tools. The research was undertaken in Sabah Softwoods Berhad (SSB) forest plantation, East Malaysia, to analyze land use in the high functionality areas. Aside from the social benefits, there has been a lack of research conducted to estimate the areas’ Net Primary Productivity (NPP) using remote sensing of biophysical characteristics. The NPP quantifies the biomass generated by the green plants, which provides the chemical energy that drives most of the biotic processes on the Earth. The NPP calculated from the study for all land features in the study areas ranged from 39.33 gCm−2 month−1 to 1498.00 gCm-2 month-1. This work has established a new NPP assessment for all land features in the Brumas forest plantation in East Malaysia. Because NPP is an estimate of the earth’s living biomass, this type of approach should be used to develop biomass maps to meet human requirements on the planet.

Soil moisture assessment on production land is gaining more attention as one of the critical factors and had a remarkable impact on agriculture production, life as well as global warming. Bibliometric analysis is performed by extracting datasets from SCOPUS from 2000 to 2020 to analyse soil moisture using remote sensing study and progress in the last two decades. The outcome indicates that study on the development of soil moisture monitoring tools using remote sensing has been increasing especially for international cooperation articles. International Geoscience and Remote Sensing Symposium (IGARSS) recorded the most productive journal published articles in this field. Among the top active countries that produce most articles were the USA followed by China. The current keywords search on soil mechanism and satellite technology frequently searched in this field. Global issues that focus on the relationship between soil moisture and environmental forecast such as drought, climate change and global warming by using remote sensing technology needed more high impact research outputs in the future.

Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula.

Oil palm is the most important crop in Malaysia. It plays a key role in maintaining carbon balance and it possesses high economic value. However, oil palm productivity is really sensitive to enviro...

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.

Rice monitoring is one of the main issues in rice productivity. Farmers face difficulties in monitoring their rice fields due to climate change, soil conditions, age of the farmers and time consumed to monitor the whole area. Remote sensing technology is one of the alternatives to monitor rice field. The advancement of unmanned aerial vehicle (UAV) technology has been rapidly growing and frequently used in the agriculture industries to monitor crop condition. The objectives of this research are creating crop growth map using aerial imagery and object-based image analysis (OBIA) technique, and validating the normalized difference vegetative index (NDVI) value in rice field map using soil plant analysis development (SPAD) and GreenSeeker data. The multispectral image is processed using OBIA to produce crop growth map. The crop growth map produced is embedded with information that is able to indicate the health status of the rice crop using NDVI. This research was carried out at a paddy field planted using PadiU Putra variety in Ladang Merdeka, Ketereh, Kelantan (0.79 ha). The results from this research show that OBIA method can classify vegetation and non-vegetation to produce crop growth map. NDVI map has a strong correlation with Greenseeker data at 0.893 with positive correlation at 0.05 compared to SPAD meter. The crop growth map allows farmers to improve their rice farm monitoring more effectively using remote sensing technique.

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease.

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.

Various classification methods have been applied for low resolution of the entire Earth’s surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer’s Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

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.

Normalized Difference Water Index (NDWI) and MODIS Normalized Difference Vegetation Index (NDVI) showed a relationship with annual rainfall and soil moisture. A very significant relationship found in polynomial relationship between annual rainfall with NDVI and MODIS NDVI (r2 = 0.830 and 0.983). Higher rainfall was associated with higher NDVI value for both, surface reflectance imagery and MODIS NDVI. This study found no significant relationship in in situ measurement between soil moisture content and mean NDWI. This study suggests the insignificant was associated with insufficient in-situ data. Whereas annual rainfall and mean NDWI for long term period was r2 = 0.349 which indicate moderate relationship. Further study is require to investigate the NDWI value for long term period and in situ measurements in order to provide insufficient in situ analysis data.

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.

Coastal landscapes are not only supporting the most productive and ecologically valuable ecosystem but are also fast changing, caused by both anthropogenic and natural processes. Changes in the form of diminishing vegetation cover, water body and increasing urbanization in Terengganu, East Coast of Peninsular Malaysia, for the years of 2000 and 2017 were assessed using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) product. Images were processed based on Erdas Imagine software and then projected to World Geodetic System (WGS 84) coordinates based on ArcGIS 10.0. Significant reduction is detected in vegetation cover, from 46.5% in the year 2000 to 26.6% in 2017, coinciding with an increase in urban areas (from 3.3 to 33.6%). Changes due to urbanization raise concern over the loss of coastal landscape and may impact its resilience, so it may no longer be able to provide key ecosystem services. This understudied ecosystem deserves to be conserved for its ecosystem services. The paper argues that looking at the data presented, the resilience or the capacity of the Terengganu coastal landscape in maintaining its ecosystem services in the near future might have been compromised. Recommendations on how these valuable landscapes could be best conserved for social and ecological sustainability are put forward.