Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.

Tingkat Kenyamanan Di Hutan Kota Patriot Bina Bangsa Kota Bekasi

The development of urban area was accured quickly and lead to many environmentalproblems such as the rising of temperatures and decreasing of environmental quality. Greenopen space (RTH)was needed to overcome those problems. The objectives of the researchwere to figure out the characteristics of vegetation in Patriot Bina Bangsa Urban Forest(including the species, density, broad canopy coverage), to figure out comfort index ofPatriot Bina Bangsa Urban Forest and to find out the visitor’s perception about comfortsvalue of facilities. The analysis of vegetation was employed as the method of data collectionsensus method was used as the sampling method, and temperature humidity index (THI) wasused to determine the comfort level. Interview techniques with random sampling method wasused to determine the visitors perception. The result showed that the vegetationcharacteristics of Patriot Bina Bangsa Urban Forest took effects to temperature andhumidity. High density of tree could decrease the air temperature and increase the humidity, where dense canopy class had air temperature at 27.6 ºC and humidity at 80.1%; moderatecanopy class had air temperature at 29.1 ºC and humidity at 73.2%; sparse canopy class hadair temperature at 30.1 ºC and humidity at 70.5%. Based on the temperature humidity index(THI), Patriot Bina Bangsa Urban Forest was categorized as uncomfortable, with THI values>26. Most of visitors (77.72%) believed that the facilities wich exist in Patriot Bina BangsaUrban Forest ware categorized good.Key words :Patriot Bina Bangsa Urban Forest, urban forest comfort, city