Lake Tempe is located in three districts, namely Wajo, Sidenreng Rapang, and Sopeng, South Sulawesi. The water quality in Lake Tempe needs to be considered both the quality and the quantity of the water. Total Suspended Solid (TSS) is one of the calculations and analysis of air quality. The large of TSS distribution can overcome the effects of sedimentation thereby reducing the need for lakes in saving water. TSS distribution at Lake Tempe can be accessed through Sentinel2B imagery with acquisition time 10 April 2019 and spatial resolution of 10 meters. The algorithm used is NSMI (Normalized Suspended Material Index) algorithm then the results are compared with TSS measurements result in the field. The time of the study was conducted in April 2019 at Lake Tempe, South Sulawesi. The result from samples showed various TSS value which is in the range of 65 mg/L to 203 mg/L with R2of 0.1194 and standard deviation of 8.7106. High TSS value on the banks of the North lakes also had high sedimentation. Low TSS value are in the middle of the lake with small sedimentation and deeper lake

Cities are centres of economic growth with fascinating dynamics, including persistent urbanisation that encroaches adjacent arable lands to build urban physical features and sustain services offered by urban ecosystems. Even though industrial revolution, economic dynamics, and environmental changes affect spatial feasibility for housing, complex urban growth is always followed by the development of environmentally friendly cities. However, with such quality having multiple facets, it is necessary to assess and map liveable areas from a more comprehensive and objective perspective. This study aimed to assess, map and identify the biophysical quality of an urban environment using a straightforward technique that allows rapid assessment for early detection of changes in the quality. It proposed a multi-index approach termed the urban biophysical environmental quality (UBEQ) based on spectral characteristic of remote sensing data for residential areas calculated using various data derived from remote sensing. Statistical analyses were performed to test data reliability and normality. Further, many indices were analysed, then employed as indicators in UBEQ modelling and tested with sensitivity and factor analysis to obtain the best remote sensing index in the study area. Based on PCA Results, it was found that the built-up land index and vegetation index mainly contributed to the UBEQ index. The generated model had 86.5% accuracy. Also, the study area, Semarang City, had varying UBEQ index values, from high to low levels.