The aim of this paper is to review the potentialities and major methodological challenges
of integrating remote sensing (RS) and geographic information system (GIS) with socioeconomic data
from published articles or book chapters. RS and GIS combined with social science (SS)(termed as
geoinformation technology) serve many applications for sustainable management and monitoring of
the environment. This combined approach gives more accurate results than the single one. It makes
information available about the trend and pattern of land use and land cover change (LUCC) with
socioeconomic variables like population, demographic or income. This combined study which links
RS and GIS with socioeconomic data can also be used successfully for monitoring transmission rate
of disease and mapping or preparing vulnerability index. For impact assessment and modelling, this
combined technology provides better results than the single one. There are some methodological
problems for the researchers to link completely two different disciplines as the object of study and
observational unit is completely different. However, this interdisciplinary study is gaining popularity
day by day to researchers from different disciplines as well as decision makers.
Monitoring of land use change is crucial for sustainable resource management and development planning. Up-to-date land use change information is important to understand its pattern and identify the drivers. Remote sensing and geographic information system (GIS) have proven as a useful tool to measure and analyze land use changes. Recent advances in remote sensing technology with digital image processing provide unprecedented possibilities for detecting changes in land use over large areas, with less costs and processing time. Thus, the objective of this study was to assess the land use changes in upper Prek Thnot watershed in Cambodia from 2006 until 2018. Geospatial tools such as remote sensing and GIS were used to process and produce land use maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8. The post-classification comparison was conducted for analysing the land use changes. Results show forest area was greatly decreased by 1,162.06 km2 (33.67%) which was converted to rubber plantation (10.55 km2 ), wood shrub (37.65 km2 ), agricultural land (1,099.71 km2 ), built-up area (17.76 km2 ), barren land (3.65 km2 ), and water body (14.69 km2 ). Agricultural land increased by 1,258.99 km2 (36.48%), while wood shrub declined by 161.88 km2 (4.69%). Rubber plantation, built-up area, barren land, and water bodies were increased by 10.55 km2 (0.31%), 33.64 km2 (0.97%), 4.87 km2 (0.14%) and 15.89 km2 (0.46%), respectively. The decrease of forest and wood shrub had resulted due to population growth (1.8% from 2008 to 2019) and land conversion for agricultural purposes. Hence, this study may provide vital information for wise sustainable watershed’s land management, especially for further study on the effect of land use change on runoff in this area.
The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management
issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling
their growth and decline. This study focused on the detection of urban tree species by considering three types of tree
species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these
three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of
maximum likelihood classification and support vector machines. These methods were then compared with object-based
image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color
attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated
that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy.
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.