Coronavirus disease (COVID-19) was firstly identified in Wuhan, China. By 23rd January 2020, China’s Government made a decision to execute lockdown policy in Wuhan due to the rapid transmission of COVID-19. It is essential to investigate the land surface temperature (LST) dynamics due to changes in level of anthropogenic activities. Therefore, this study aims (1) to investigate mean LST differences between during, i.e., December 2019 to early March 2020, and before the emergence of COVID-19 in Wuhan; (2) to conduct spatio-temporal analysis of mean LST with regards to lockdown policy; and (3) to examine mean LST differences for each land cover type. MODIS data consist of MOD11A2 and MCD12Q1 were employed. The results showed that during the emergence of COVID-19 with lockdown policy applied, the mean LST was lower than the mean LST of the past three years on the same dates. Whereas, during the emergence of COVID-19 without lockdown policy applied, the mean LST was relatively higher than the mean LST of the past three years. In addition, the mean LST of built-up areas experienced the most significant differences between during the emergence of COVID-19 with lockdown policy applied in comparison to the average of the past three years.

Mangrove forests provide numerous valuable ecosystem services and can sequester a large volume of carbon that can help mitigate climate change impacts. Modeling mangrove carbon with robust and valid approaches is crucial to better understanding existing conditions. The study aims to estimate mangrove Above-Ground Carbon (AGC) at Loh Buaya located in the Komodo National Park (Indonesia) using novel Extreme Gradient Boosting (XGB) and Genetic Algorithm (GA) analyses integrating multiple sources of remote sensing (optical, Synthetic Aperture Radar (SAR), and Digital Elevation Model (DEM)) data. Several steps were conducted to assess the model’s accuracy, starting with a field survey of 50 sampling plots, processing the images, selecting the variables, and examining the appropriate machine learning (ML) models. The effectiveness of the proposed XGB-GA was assessed via comparison with other well-known ML techniques, i.e., the Random Forest (RF) and the Support Vector Machine (SVM) models. Our results show that the hybrid XGB-GA model yielded the best results (R2 = 0.857 in the training and R2 = 0.758 in the testing phase). The proposed hybrid model optimized by the GA consisted of six spectral bands and five vegetation indices generated from Sentinel 2B together with a national DEM that had an RMSE = 15.40 Mg C ha−1 and outperformed other ML models for quantifying mangrove AGC. The XGB-GA model estimated mangrove AGC ranging from 2.52 to 123.89 Mg C ha−1 (with an average of 57.16 Mg C ha−1). Our findings contribute an innovative method, which is fast and reliable using open-source data and software. Multisource remotely sensed data combined with advanced machine learning techniques can potentially be used to estimate AGC in tropical mangrove ecosystems worldwide.

Abstrak: Penelitian ini bertujuan untuk (1) melakukan penilaian tingkat kerusakan permukiman yang terkena banjir lahar, (2) menganalisis tingkat kerusakan permukiman akibat banjir lahar di daerah penelitian dan (3) menganalisis pengalokasian ruang pembangunan pemukiman berbasis tingkat kerusakan permukiman pasca banjir lahar. Metode yang digunakan adalah GPS Tracking untuk mengetahui luapan banjir lahar, klasifikasi tingkat kerusakan permukiman berdasar kriteria yang telah ditetapkan. Spatial autocorrelation untuk mengetahui pola kerusakan permukiman. Hasil penelitian menunjukkan bahwa kerusakan bangunan permukiman akibat banjir lahar tidak hanya disebabkan oleh jarak rumah terhadap sungai tetapi disebabkan oleh material bangunan permukiman tersebut. Pola spasial yang dihasilkan adalah 0,68 (mengelompok) untuk Roboh/Hanyut, 0,62 (mengelompok) untuk Rusak Berat, 1,05 (mengelompok) untuk Rusak Ringan, 0,64 (mengelompok) untuk Rusak Sedang, dan 1,21 (mengelompok) untuk Tidak Rusak.Kata Kunci : erupsi, banjir lahar, kerusakan permukiman Abstract: The aim of this study are (1) to assess the damage of settlement due to lahar flood in study area, (2) to analyze the damage of settlement and (3) to analyze the allocated space of settlement development based on classification of damage settlement. Methods that used in this study are GPS Tracking to know the distribution of lahar flood, classification of damage settlement based on predetermined criteria and spatial autocorrelation to know the pattern of damage settlement. The result of this study is showing that damage settlement due to lahar flood is not only caused by the house distance to the river but also by the materials of it. The spatial pattern of damage settlement is 0,68 (clustered) for Collapse, 0,62 (clustered) for High Damaged, 1,05 (clustered) for Low Damaged, 0,64 (clustered) for Medium Damaged) and 1,21 (clustered) for No Damaged.

Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.

Provinsi Sumatera Utara di Indonesia tercatat telah memproduksi komoditas ekspor yang sangat tinggi untuk Ikan Kerapu. Hal ini harus dipertahankan sebagai upaya menjaga keberlangsungan ekonomi perikanan. Salah satu cara mengembangbiakan Ikan Kerapu adalah dengan marin akuakultur (marikultur) yang sangat bergantung pada ekologi lautan seperti keberadaan klorofil-a, Suhu Permukaan Laut (SPL), Muatan Padat Tersuspensi (MPT) dan topografi kedalaman laut (batimetri). Kondisi ekologi lautan yang sangat mudah berubah menghendaki pemantauan secara berkala. Penelitian ini memiliki tujuan pertama yaitu mengetahui kemampuan data penginderaan jauh untuk mengekstraksi parameter -parameter yang digunakan untuk kelayakan lokasi marikultur Ikan Kerapu. Citra Landsat 8 digunakan untuk mengetahui klorofil-a, SPL dan MPT, sedangkan data batimetri didapatkan dari ETOPO1, sebuah data topografi skala global yang memiliki perekaman permukaan lahan (terrain) hingga dasar lautan. Tujuan yang kedua adalah mengetahui keakuratan kesesuaian lokasi marikultur yang dihasilkan oleh pemrosesan citra pada Google Earth Engine (GEE). Hasil penelitian dibandingkan dengan peta referensi mengenai lokasi marikultur yang diperoleh dari Lembaga Penerbangan dan Antariksa Nasional (LAPAN) dan menampilkan hasil pengujian matriks akurasi sebesar 80 %. Hal ini membuktikan data penginderaan jauh dapat digunakan untuk membantu menentukan lokasi marikultur Ikan Kerapu dan GEE adalah platform yang sesuai untuk pemantauan secara berkala dengan kemampuan olah citra melalui komputasi awan (cloud computing) sekaligus dapat melakukan analisis penjenjangan bertingkat.