Defining fire issues in Malaysia and Indonesia through recent satellite technology: a review on MODIS fire detection and burned mapping

Forest fire, a global phenomenon, often leads to environmental degradation such as habitat damage and trans-boundary haze. In response to growing concerns over the burning of peat swamp forests, researchers have begun developing methods of detecting and mapping forest fire. In addition, substantial progress has been made in forest planning and fire management, as well as in developing fire detection method using modeling techniques. This paper reviews current forest fire detection and burned area mapping methods that have been applied and studied in most affected area in Malaysia and Indonesia. This paper is also discussing other methods of remote sensing in forest fire detection and burned area mapping. Future research of using MODIS remote sensing technology in forest fire detection and mapping in both countries were also deeply described

Forest Fire Hazard Rating Assessment In Peat Swamp Forest Using Integrated Remote Sensing And Geographical Information System

Forest fire can be a real disaster, regardless of their causes, be it human activity or nature. While it is difficult to control nature, it is possible to map different hazard levels thereby minimizing fire hazards and avoid potential damage. Satellite data plays an important role in detecting and mapping forest fires, involving different types of vegetation. This study was conducted with two objectives: first by applying remote sensing techniques to delineate fuel types map and burnt areas in peat swamp forest; secondly was to develop a fire hazard modelling and mapping of fire hazard rating areas using the Geographical Information System (GIS). A fire prone peat swamp forest located in Penor, Pahang was selected for the study. A colour composite image from Landsat Thematic Mapper (TM) was transformed using Tasseled Cap Transformation (TC) and a fuel types map was produced. Roads and canal were digitized and developed as layers using ArcGIS 8.2. These layers were composite and four categories of forest fire hazard ranging from extreme to null were automatically derived. The final forest fire hazard rating map is presented in ArcView 3.1. In conclusion, almost 50% of the study areas were classified as ‘low’ hazard and only 10% of the areas were classified as ‘extreme’ hazard. As a result, the fire hazard map can be used for better forest fire management activities for that area

Coastal Landscapes of Peninsular Malaysia: The Changes and Implications for Their Resilience and Ecosystem Services

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

Combining Moderate and High Resolution of Satellite Images for Characterizing Suitable Habitat for Vegetation and Wildlife

Combining different resolution of remote sensing satellites becomes a unique approach for vegetation and wildlife habitat assessment study. Remote sensing technology can reach land and water on the Earth's surface, and it can interpret signals from spectral responses. When these techniques are combined with Geographical Information Systems (GIS), land can be monitored in a variety of ways. Meanwhile, changes in land use led to changes in vegetation on the ground, with natural vegetation being removed from natural forests, leaving a degraded forest. This issue was not investigated for assessing habitat suitability for important plantations such as Eucalyptus plantation. Therefore, the study employed remote sensing and Geographical Information System (GIS) to model suitability of habitat to live and to survive in the Eucalyptus plantation. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from a mathematical equation can demonstrate intensity of greenness of green vegetation in particular area and time, and availability of soil moisture, respectively, is very suitable to model the greenness of the area. WorldView-2 satellite image was pre-proceed, proceed, and classified to produce land use indicator in Sabah Softwoods Berhad plantation majoring Eucalyptus spp. tree planted in Tawau, Sabah. Sentinel and Landsat 8 image were used for vegetation and water stress indicator were downloaded from Land Viewer application. Net Primary Productivity (NPP) at monthly scale was also calculated and ranked the productivity for the suitability mapping. Climatic condition based on monthly precipitation and seasonality derived from ASEAN Specialized Meteorological Centre (ASMC) was employed for ranking its suitability value. In this study, natural forest and oil palm plantation is tested to developed suitability map for vegetation and wildlife habitat to live with. All indicators were ranked 10 to 40 presenting benefit and usefulness of the indicator to vegetation and wildlife in the study area. Then, final classification was made from accumulation of those indicators into 0 to 200 (Not suitable to Highly suitable). The results showed 59.9% of the area classified as moderately suitable, 36.9% highly suitable, 3.2% least suitable and no area was classified as not suitable. This type of study assisted forest managers and policymakers for better managing of their forests for better life of trees and wildlife under their management. The methodology adapted in the study is ecologically sounded and economically viable to be modified and complied in Sustainable Forest Management (SFM) in Malaysia and other tropical forest regions

Remote sensing indices for mangrove health assessment

This paper is attempts to review the application of remote sensing technology for mangrove health assessment derived from spectral information. One of the most valuable forest lands in the coastal area is mangrove forests. Mangroves are economically and environmentally important for maintaining global conservation. Increase in population is putting high pressure in coastal areas with conversion of many mangrove forests to other land usages, including infrastructure, aquaculture, rice and salt production. The conversion affects water quality and hence, increases pressure to mangrove health. The health of mangrove can be assessed by employing remote sensing indices. Remote sensing technology is a very important technique for assessing mangrove health by derivation of spectral reflectance and conversion into mathematical equation. Normalised Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are discussed in this paper. This paper also highlights the potential of the indices to be integrated with drought index for drought modelling systems. In addition, it explores various cases studied in Peninsular Malaysia and elsewhere to emphasise the utilisation of the indices in various locations of mangrove areas around the globe

Application of remote sensing and GIS in monitoring forest, rubber and oil palm drought study

Tropical forest together with oil palm and rubber play huge significant role in providing biodiversity, maintaining ecosystem, maintaining carbon balance and have economic value for Malaysian economy. However, drought and “El Nino” phenomena that happening in Malaysia have gave dynamic impact on reducing yield quality and quantity of forest and the plants. To study drought based on remote sensing techniques, continuous time-series of satellite images of the study site collected by using WorldView-2 satellite image. The images were employed to develop important vegetation indices inclusion of Normalised Difference Vegetation Index (NDVI), the Normalised Difference Water Index (NDWI), Water Index (WI), Simple Ratio (SR), Stress Index (SI), Difference Vegetation Index (DVI) and Ratio Vegetation Index (RVI). The indices were applied on the three different habitats to monitor drought of the sampling points. Then lastly, the detail analysis and interpretation of maps throughout the study period was done Geographic Information Systems (GIS) of segmentation analysis in spatial analyst

Mapping human impact on Net Primary Productivity using MODIS data for better policy making

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

Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions

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

Monitoring vegetation drought using MODIS remote sensing indices for natural forest and plantation areas

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

Character association and selection of breeding line based on Morphophysiological characteristics and tensile strength in Hibiscus cannabinus L.

The nonlinear stability analysis of a ferrofluid layer system is formulated mathematically. This system considered the upper and lower free isothermal boundary with the system heated from below. A mathematical formulation is produced to study the behaviour of the chaotic convection in a ferrofluid layer system using Galerkin truncated expansion. The Boussinesq approximation is opted with the existence of internal heating and the magnetic number. It is found that the transition to chaos in this present study is identical to the Lorenz attractor and thus validate the method and analysis of this study. The impact of elevating the internal heat generation is found to hasten the instability of the system and as for the magnetic number, at M1 = 2.5 the homoclinic bifurcation occurs and thus accelerates the convection process