Canopy height estimation from lidar data using open source software compared with commercial software

The goal of this study is to analyze the performance of Open Source Software (OSS) towards the generation of Digital Terrain Model (DTM) and Digital Surface Model (DSM), further on estimates the canopy height by using Light Detection and Ranging (LIDAR) data. Generation of DTM and DSM are very important in this research to ensure that better canopy height can be modeled. DTM and DSM commonly known as a digital representation of earth surface topography where DTM only represent the ground surface while DSM represent all the features including buildings and trees. Many software that have a function to generate DTM and DSM were developed recently. However, most software has been commercialized; therefore it requires a high expenditure to own the software. Advanced technology has lead to the emergence of the growing OSS. OSS is software that can be downloaded for free via the internet. By taking the forestry area of Pekan, Pahang for this research, LIDAR data for that particular area is processed by using the OSS Geographic Resources Analysis Support System (GRASS). To determine the effectiveness and capability of GRASS in the DTM and DSM generation, the same data were processed using commercial software which is TerraScan so that the result can be compared, further on better canopy height can be modele

Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim

Urban area refers to cities which have highly heterogeneous objects with complex landscape. Variation land-cover features, which include natural and man-made objects, lead to the advent of features that are spectrally very similar. For example, trees and grass as well as building roofs and roads often have similar spectral response that always had been misclassified yet difficult to distinguish. The complexness of urban features require high spatial as well as high spectral resolution image especially when trying to extract the land-cover objects. Advanced in remote sensing technology has tremendously strengthens the spatial and spectral techniques, which will greatly affects urban environment study. The emerging of high spectral resolution image with submeter level of accuracy lead to high potential in order to identify detailed urban landcover features. By adopting object based approached instead of pixel based will avoid the 'salt and pepper' effect that will decrease the accuracy of land-cover classification. Using Worldview-2 multispectral satellite image as a primary data, together with ancillary data which include normalized Digital Surface Model (nDSM) derived from Light Detection and Ranging (LIDAR) data and indices layer, the image segmentation process utilizing multiresolution segmentation algorithm was conducted. Twelve segmentation levels were constructed in order to create meaningful image objects before going through the classification process. Three supervised object based classifier namely Support Vector Machine (SVM), BAYES and K-Nearest Neighbour (KNN) were tested in order to identify which classifier gives the best classification result of the urban area of the study. Thirteen sets of experiment were created, which consist of different combination bands (8 multispectral band of worldview-2, panchromatic band, indices layer, texture image, spectral transformation image) to be tested by each classifier. The results from the study indicate statistically significant difference in classification accuracy between each classifier and experiment sets: SVM outperforms BAYES and KNN as produced highest overall accuracy (87.93%), however based on Kappa statistics per class, user's and producer's accuracy, as well as visual examination and overall accuracy performance, BAYES with overall accuracy of 85.51% has depicted to have the best land-cover classification accuracy result

Quantifying the Air Temperature Reduction with Greenery in UiTM Shah Alam: A Microscale Study

Growth in cities population has caused urban sprawl which is the key factor in the issues of high temperatures as well as UHI in many countries. This issue has affected the urban microclimate as well as the indoor and outdoor conditions of human thermal comfort. This issue is also aggravated by the replacement of natural greenery area with building and other man-made features. For that reason, greening the cities, as part of bioclimatic concept of build environment, could be the way to decrease the outdoor temperature and making the surrounding more comfortable. To understand this issue further, ENVI-met software was used to simulate all activities either natural or man-made to attain accurate prediction and evaluation for microclimate changes in certain area. For this study, the simulations were run in three scenarios of pavement, asphalt pavement without plants (scenario 1), concrete pavement without plants (scenario 2), and asphalt pavement with plants (scenario 3). Plants were design in area surrounding the building and in courtyard consisting of pine trees and hedges of 2 metre height. The result shows that greenery plants can influence air temperature and airflow in the surrounding thus improving thermal comfort in the area. Existing plants can decrease temperature from 0.5°C to 2.3°C and air velocity become slower at 0.05 m/s to 0.15 m/s. Overall, although the changes are at small scale, it is shown that plants are able to improve microclimate surrounding better towards thermal comfort standards