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.

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.

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.

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 environmental changes especially during the long-term dry season. This has warranted studies on frequent observation of dry season for the plantation of oil palm. In this study, Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance data were utilized to generate an index for the crop assessment at Felcra Mendom Oil Plantation area, Negeri Sembilan. The indices of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and shortwave indices, namely Shortwave Infrared Water Stress Index (SWISI), at three different channels of 2, 5, and 6 were utilized. The main aim is to identify drought year based on MODIS-SPI-based index calculated and evaluated indices developed from MODIS images. The precipitation data retrieved from Malaysia Meteorology Department starting from year 1997 until 2017 and later are classified using Standardised Precipitation Index (SPI). Then by using the data from precipitation and SPI, the MODIS images were extracted from Land Process Distributed Active Archive Center (LP DAAC). Next, the images were processed and analysed by using ENVI Version 5.3 software to extract the indices used. The SPI-3 and SPI-1 were classified year 2015 as extreme drought with ‘extreme’ and ‘mild’, respectively. Meanwhile, the vegetation indices were calculated and average showed both NDVI and EVI approaching +1.00 value at the highest NDVI is 0.79 during 2017 and the highest EVI is 0.62 during years 2005 and 2013, respectively. Both SIWSI showed similar pattern, indicating high-moisture content as the index approaching −1.00. The result does not represent the overall condition of the specific study area, but the SPI is reliable to display the whole area and the utilization of MODIS and SPI can be used in further study.

Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction.