In this research wok, three different techniques of change detection were used to detect changes in forest areas. One of the techniques used a local similarity measure approach to detect changes. This new approach of change detection technique, which used mutual information to measure the similarity between two multi-temporal images, was developed based on correspondence of the pixel values, rather than the difference in their intensity. Pixels suffering any changes will be maximally dissimilar. The study was conducted using multi-temporal SPOT 5 satellite images, with the resolution of 10 m x10 m on 5th August 2005 and 13th June 2007. The experimental results show that local mutual information provides more reliable results in detecting changes of the multitemporal images containing different lighting condition compared to the image differencing and NDVI technique, specifically in areas with less plant growth. In addition, it can also overcome the problem on selecting the threshold value. Besides, the findings of this study have also shown that band 3, which is sensitive to vegetation biomass, gave the best result in detecting area of changes compared to the others.
The use of remote sensing imagery, to some extends geographic information system (GIS), have been identified as the most recent and effective technologies to assess forest biomass. Depending on the approaches and methods employed, estimating biomass by using these technologies sometimes can lead to uncertainties. The study was conducted to investigate appropriate methods for estimating aboveground biomass (AGB) by using synthetic aperture radar (SAR) data. A total of 60187 ha in Dungun Timber Complex (DTC) were selected as the study area. Thirty seven sample plots, measuring 30×30 m were established in early 2012 covering both natural and logged forests. Phase Array Type L-Band SAR (Palsar) images that were acquired in 2010 were used as primary remote sensing input and shapefile polygons comprised logging records was used as supporting information. By using these data, two estimation methods, which were ‘stratify and multiply’ (SM) and ‘direct remote sensing’ (DR) have been adopted and the results were compared. The estimated total AGB were about 20.1 and 22.3 million Mg, from SM and DR methods, respectively. The study found that the images that incorporated texture measures produced more accurate estimates as compared to the images without texture measures. The study suggests that SM method still a viable and reliable technique for quick assessment of AGB in a large area. The DR method is also relevant provided that an appropriate type and processing techniques of SAR data are utilized.
Urban green space (UGS) in a city is the foundation of natural productivity in an urban structure. It is also known as a natural cooling device that plays a vital role in the city as an urban lung, discharging oxygen to reduce the city heat and as a wall against harmful air pollution. When urbanization happens, UGS, including the gazetted areas, is essentially converted into an artificial surface due to the population’s demand for new development. Therefore, identifying its significance is a must and beneficial to explore. The purpose of this study is to identify the 10 years of UGS change patterns and analyze the UGS loss, particularly in the affected gazetted zone. The study used available aerial imagery data for 2002, 2012, and 2017, and database record of green space. The study had classified UGS by using the Support Vector Machine (SVM) algorithm. The training area was determined by visual interpretation and aided by a land use planning map as reference. The result validity was then determined by kappa coefficient value and producer accuracy. Overall, the study showed that the city had lost its UGS by about 88% and the total gain in built up area was 114%. The loss in UGS size in the city could be compared to a total of 2,843 units of football fields, transformed forever in just 10 years. The uncontrolled development and lack of advanced monitoring mechanism had negatively affected the planning structure of green space in KL. The implementation of advance technology as a new mitigation tool to monitor green space loss in the city could provide a variety of enhanced information that could assist city planners and urban designers to defend decisions in protecting these valuable UGS.