Building density and its implications to COVID-19 health risk management: An example from Yogyakarta, Indonesia

Transmission rates of COVID-19 have been associated with the density of buildings where contact among individuals partially contributes to transmission. The research sought to analyze the spatial distribution of building density derived from satellite images and determine its implications to COVID-19 health risk management using Yogyakarta and its surrounding districts as an example. Fine-scale building distribution obtained through remote sensing data transformation was analyzed with GIS. NDBI was applied to Landsat 8 imagery;then, using multiple linear regression analysis, it was correlated to building density’s training samples generated from high-resolution imagery. The derived percent of building density (PBD) was combined with publicly available records of COVID-19 infection to assess risk. This research found that PBD could explain the uneven COVID-19 diffusion at different stages of its development. Instead of dividing regions into zones based on confirmed cases, government and public health officials should observe new cases in high-PBD districts;then, when the cases are decreasing, their attention should shift to low-PBD districts. Remote sensing data allow for moderate-scale PBD mapping and integrating it with confirmed cases produces spatial health risks, determining target areas for interventions and allowing regionally tailored responses to anticipate or prevent the next wave of infections.

Urban Heat Island Mitigation Strategy based on Local Climate Zone Classification using Landsat 8 satellite imagery

Appropriate strategies on urban climate mitigation should be formulated by considering the physical morphology of the urban landscape. This study aimed to investigate, analyze, and promote possible strategies to mitigate Jakarta’s urban heat island (UHI) phenomena. Jakarta’s local climate zone (LCZ) was classified into 17 classes using Landsat 8 data and the random forest method. Land surface temperature (LST) characteristic in each LCZ class was analyzed from 2018, 2019 and 2020. The result revealed that most of the local climate zone in Jakarta is dominated by LCZ 6 (open low-rise) and LCZ 3 (compact low-rise), which is the typical residential area in Jakarta. However, the mean LST in 2018, 2019 and 2020 showed that LCZ 3 (compact low-rise) and LCZ 7 (lightweight low-rise) are the areas that were most likely causing high surface temperature with the highest UHI intensity. During the COVID-19 pandemic in 2020, LST in Jakarta decreased drastically in some parts of the area, especially in public facility such as airport. However, the LST value in low-rise areas (LCZ 3 and LCZ 7) remains higher than the other LCZ classes. Materials of the building and land cover play a significant role in raising the land surface temperature. Therefore, mitigation strategies for urban heat islands in Jakarta should be focused on such particular areas mentioned.

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine

Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years;many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EFe), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture. [ FROM AUTHOR];Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Stability and Suitability of the Bhasan Char Island as an Accommodation for the Forcibly Displaced Myanmar Nationals (FDMN)

Assessing the dynamics of Bhasan Char is very crucial, as the Government of Bangladesh (GoB) has recently selected the island as the accommodation of the FDMN. This article critically evaluates the spatiotemporal morphological variations due to erosion, accretion, and subsurface deformation of the island through multi-temporal geospatial and geophysical data analysis, groundwater quality-quantity, and also determines the nature and rate of changes from 2003 to 2020. This is the first study in this island on which multi-temporal Landsat Satellite Imagery and seismic data have been used with geospatial techniques with Digital Shoreline Analysis System (DSAS) and petrel platform, respectively. The analysis of satellite images suggests that the island first appeared in 2003 in the Bay of Bengal, then progressively evolved to the present stable condition. Significant changes have taken place in the morphological and geographical conditions of the island since its inception. Since 2012, the island has been constantly accreted by insignificant erosion. It receives tidally influenced fluvial sediments from the Ganges-Brahmaputra-Meghna (GBM) river system and the sedimentary accretion, in this case, is higher than the erosion due to relatively weaker wave action and longshore currents. It has gained approximately 68 km2 area, mostly in the northern part and because of erosion in the south. Although the migration of the Bhasan Char was ubiquitous during 2003–2012, it has been concentrated in a small area to the east since 2018. The net shoreline movements (NSM) suggest that the length of the shoreline enlarged significantly by around 39 km in 2020 from its first appearance. Seismic and GPS data clearly indicate that the island is located on the crest of a slowly uplifting low-amplitude anticline, which may result in a stable landform around the island. Based on the analysis of historical data, it has been assessed that the current configuration of Bhasan Char would not be severely affected by 10–15-foot-high cyclone. Therefore, FDMN rehabilitation here might be safer that would be a good example for future geo-environmental assessment for any areas around the world for rehabilitation of human in remote and vulnerable island. The findings of this research will facilitate the government’s decision to rehabilitate FDMN refugees to the island and also contribute to future research in this area.

The Effect of Greenness Index on Case Fatality Rate of Covid-19 Analyzed by Using Landsat-8 Image (Case Study: DKI Jakarta)

COVID-19 is spreading into Indonesia and has reached tens of thousands of cases as of September 30, 2020. It was recommended by the American Public Health Association and the Centers for Disease Control and Prevention to remain physically active during COVID-19 quarantine by regularly visiting parks and green spaces as it can protect the body against the consequences of quarantine impacting physical and mental health. In this research, green space was monitored by using remotely sensed data. The green space distribution was obtained from the calculation of the Greenness Index from Landsat-8 Surface Reflectance Tier 1 satellite imagery processed through the Google Earth Engine platform. This study was conducted to determine the value of Greenness Index (GI), Case Fatality Rate (CFR) value due to COVID-19, and the relationship between them in 42 sub-districts in DKI Jakarta in the period of April to September 2020. Twenty-eight subdistricts (67%) showed negative correlation values that indicated that more green space in a region affects lower CFR growth.