Individual tree measurement in tropical environment using terrestrial laser scanning

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models

Geospatial approach for Landslides Vulnerability Assessment of Physical Infrastructures in Sireh Park, Johor Bahru

According to the landslide records between 1993 and 2019 in Malaysia, 171 individuals lost their lives, many others sustained injuries and numerous families evacuated. Additionally, infrastructures and vehicles incurred damage. Even though landslides present a risk to human life, environment, and infrastructures, there are few studies on landslides vulnerability in Malaysia with most of them focusing on social vulnerability neglecting the physical vulnerability. Consequently, a dearth of information on the vulnerability of an area to landslides may amplify the risks associated with landslides, and the relevant authorities in both the landslide-prone and affected regions may not implement the appropriate mitigation measures. The aim of this study is to estimate vulnerability of elements at risk to landslide in the hazard’s affected area of Sireh Park. An indicator based method was used to calculate the relative vulnerability index for each of the elements at risk. In applying the method, experts assigned weights to the indicators and their respective sub indicators based on their significance to landslides vulnerability. The indicators were grouped into clusters, the total values of weights for all the clusters equals to 1. The results of the study showed that about 252 elements at risk are exposed to landslides in Sireh Park, out of which 226 (89.7%) are buildings, while 26 (10.3%) are roads. The general level of buildings' vulnerability to landslides in the study area is relatively low, however about 20.4% of the buildings display a high degree of vulnerability. Meanwhile, the general vulnerability of roads sections to landslides is averagely high, with about 54.8% of the roads sections exhibiting a high degree of vulnerability while 16.1% exhibits low degree of vulnerability. The study can help stakeholders to identify locations with high landslide vulnerability and guide the development of mitigation measures and emergency preparedness plans to reduce the potential impact of landslides in the study area

SAR data analysis for wave spectrum, bathymetric, and coastal information

In this study, JERS-1 SAR data were examined and analyzed for extraction of coastal information, namely: (i) wave spectra, (ii) sea bottom topography, (iii) mangrove species classification, and (iv) mangrove forest biomass. The wave spectra were extracted using Fast Fourier Transform whilst, the TNO model were examined and analysed for the relationship between the backscatter and the sea bottom topography. The classification of SAR data to mangrove species level were performed using combined inparametric-parametric approach together with image segmentation technique. The allometric relationship between tree features and total weight were used as basis for modelling the biomass from SAR backscatter. The results of this study indicates that wave spectra can be extracted from JERS-1 SAR data, but it is rather restricted to certain sea condition to enable sea bottom topography be modelled from the backscatters. SAR data were found able to map mangrove at species level with an accuracy of 62 percent, and were also found as good estimator for biomass in the range 301-400 ton/ha with an overall 50 percent accuracy, at par with in-situ survey

Comparison of jers-1 and radarsat synthetic aperture Radar data for mapping mangrove and its biomass

This paper has reviewed comparison of classification of mangrove forest at species-level, and estimation of mangrove biomass using JERS-1 SAR and Radarsat SAR (standard mode) data. Both of these comparisons were made at selected test site in Sungai Pulai Mangrove Forest Reserve in Malaysia. The results demonstrated the utility of SAR data as potential source in mapping mangrove classes and indicator for biomass. Although there has been limited availability of exhaustive sampling points done accessibility at the test site, but the results indicated the evidence of C and L band utility for mangrove mapping and biomass estimation

Radiometric and geometric information content of TiungSAT-1 MSEIS Data

In the last two decades, satellite remote sensed data have been widely used for assisting natural resources management and environmental protection works. in both application areas, there are various level of utilisation of remote sensing techniques. In most developed nations such techniques have been in place as an operational tool white in most developing countries effort are still underway to establish operational applications. This include Malaysia, where remote sensing application at user agencies are still at the semi-operational level due to various reasons. In the context of Malaysia, (in the late 70's to early 90's) the hindering factors can be categorised into: (1)lack of infrastructure and facilities to enable such applications of the technology to be optimised, and (2)lack of expertise within relevant organisations that enables them to undertake such applications. the former factor is now completely addressed with the government's emphasis on provide computing facilities within all user agencies, and in fact most of the relevant agencies already have minimum capacity to process satellite remote sensing data for specific applications. The latter is an on-going process satellite all relevant agencies where programmes to train personnel to the expert level are carried out as a long term commitment

Utilization of polarization SAR Data for mapping coastal zone vegetation

Coastal vegetation is one of the important natural ecosystems that act as protection from sea wave erosion phenomena and supply oxygen to the surrounding organisms. POLSAR data with multi-band and multi-polarization is selected for these studies which are acquired on September 19, 2000 for coastal vegetation mapping. Two methods are performed to CVV, LHH and LHV bands: polarization signature analysis and comparison of classification between two clustering techniques (Fuzzy-k-mean and ISODATA). The result shows that L band with HV polarization can distinguish coastal vegetation better than CVV. Furthermore, classification results of LHH and LHV shows that the Fuzzy-k-mean clustering is provided accuracy of 76% and also with kappa statistic of 70%

Soil moisture estimation of drained peat area using TOPSAR data

Three empirical and theoretical radar backscatter models were examined in this study to characterise radar backscatter of TOPSAR data over coastal lowland of Sadong Simunjan River Basin, Sarawak, Malaysia. The main objective of this paper is to analyse the relationship between radar backscatter of TOPSAR data to degree of wetness of drained peat-land. The analysis of these models were examined using varying terrain-and-sensor related parameters namely surface roughness, dielectric constant, incidence angle, polarization and frequency, respectively. The results of this study indicate good relationship between moisture content and radar backscatter (RMSE < 5.0) in empirical model. However, this relationship is found rather restricted in the theoretical models due to its sensitivity to very moist to wet nature of the study area

Calibration of thermal infrared channels of Feng Yun satellite data

Most of the obtained satellite data cannot get away from radiometric errors resulted from the effect of the atmosphere and sensor alignment movement. Therefore, post-launch calibration is one of the requirements to rectify all errors to ensure high accuracy of earth’s geophysical or biophysical information in the satellite data. The calibrated satellite data will represent the significant absolute values of ground features signal. In the present study, Feng Yun 1D (FY-1D) one of the Chinese’s meteorological satellites will be calibrated to obtain sea surface temperature of the South China Sea. The calibration coefficient of gain and offset for FY-1D thermal infrared channels (10.3 – 11.3µm and 11.5 – 12.5µm) will be determined using the linear plot of brightness temperature and ground truth temperature. The split window technique will be employed to derive the values of sea surface temperature from FY-1D thermal infrared channels. Result from this study showed that the gain and offset for FY-1D thermal infrared channels was 3.4617 and 170.87, respectively. The analysis was carried out by comparison to the sea surface temperature values between ground truth data acquired from Malaysian Meteorological Department and FY-1D using z-test. The analysis showed that the value of gain and offset were significant at z = 0.1418 which is below than 1.96 at 95% confidence level. In addition, the difference value of average sea surface temperature between ground truth observation and FY-1D data is 0.34ºC