Steam power plant construction and operation is an effort to meet electricity needs. In Indonesia, two steam power plants were built and changed the landscape in Cirebon. The presence of Cirebon steam power plants has disturbed the community and potential to decrease air quality. This study aims to estimate air quality changes around the power plants based on remote sensing satellite imageries. The main data in this study obtained from Landsat-8 OLI (2019) and Landsat-7 ETM (2004) satellite imageries were processed with four parameters of air quality algorithm namely PM10, CO, SO2, and NOx on AOI with ranging of 2000 m from the source point. Validation uses comparative data from MODIS and Sentinel-2 MSS satellite imageries in the same period. Changes analysis in air quality used the Mann-Whitney method (U-Test). This research shows that the Landsat series satellite imagery is suitable to be used as the main data for estimating air quality because it has a similar pattern to comparable data. The Cirebon PLTU operation caused a significant increase in CO levels of 1.25 mg/l on a wide range. In other air quality parameters such as PM10, SO2 and NOx were decreased.

Climatic factors are an abiotic component that determines ecosystem characteristics. Rapid development activities require capable observation instruments and able to provide a variety of weather data to analyze their impact on the environment. This study aims to develop an instrument for measuring temperature and humidity as an essential component of climatic factors based on the DHT11-Arduino microsensor. Tests carried out at 30 scattered points in Cikarang Raya, Bekasi Regency, West Java. Data from the DHT11-Arduino microsensor measurement results are compared with the results of ground measurement and satellite imageries data in the same period through statistical tests. This research shows the DHT11-Arduino microsensor is able to measure temperature and humidity in Cikarang Raya with a significance level of 0.05-0.01. The correlation between microsensor and the comparative data in observing the temperature reached 0.934, while the humidity reached 0.687. The distribution of temperature and humidity of the instruments shows a similar pattern. Using DHT11-Arduino microsensor to observe temperature and humidity has proven to be feasible and able developed to obtain climatic factors data as part of sustainable ecosystem management.

Ekspansi lahan terbangun melalui kehadiran pemukiman dan kawasan industri berimplikasi pada berkurang lahan bervegetasi di wilayah Cirebon dan sekitarnya. Kondisi menyebabkan peningkatan suhu yang berpotensi memunculkan urban heat island. Perubahan suhu dapat diprediksi menggunakan pemodelan spasial dinamis sebagai bagian dari proses perubahan lansekap. Penelitian ini bertujuan untuk memprediksi dinamika suhu permukaan di wilayah Cirebon dan sekitarnya menggunakan algoritma Artificial Neural Network-Cellular Automata (ANN-CA) dengan melibatkan variabel spasial seperti kepadatan bangunan, kerapatan vegetasi, dan kepadatan jaringan jalan. Sumber data yang digunakan dalam penelitian ini berasal dari citra Landsat-5 TM dan Landsat-8 OLI pada tahun 1999, 2009, serta 2019, sedangkan data jaringan jalan berasal dari OpenStreetMaps. Suhu permukaan diperoleh dari kanal termal yang diolah menggunakan Radiative Transfer Equation. Sementara variabel lainnya diperoleh dari Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) dan line density. Penelitian ini menunjukkan suhu permukaan hasil pemodelan ANN-CA pada tahun 2019 memiliki rerata sebesar 22,61 °C. Model ini memiliki overall accuracy 0,63 dan overall kappa sebesar 0,52. Bila dibandingkan dengan nilai aktual, model ini memiliki nilai r-square mencapai 0,80 dengan selisih sebesar 0,54 °C yang layak untuk prediksi suhu permukaan di masa mendatang. Model ANN-CA menunjukkan sebaran suhu permukaan yang lebih tinggi berpusat pada wilayah kota dan peri-urban. Kajian terkait prediksi suhu permukaan diharapkan dapat menjadi perhatian utama dalam mewujudan resiliensi perkotaan.

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