Remote sensing and GIS application for monitoring drought vulnerability in Indonesia: a review

Agricultural drought is one of the hydrometeorological disasters that cause significant losses because it affects food stocks. In addition, agricultural droughts, impact the physical and socio-economic development of the community. Remote sensing technology is used to monitor agricultural droughts spatially and temporally for minimizing losses. This study reviewed the literatures related to remote sensing and GIS for monitoring drought vulnerability in Indonesia. The study was conducted on an island-scale on Java Island, a provincial-scale in East Java and Bali, and a district-scale in Indramayu and Kebumen. The dominant method was the drought index, which involves variable land surface temperature (LST), vegetation index, land cover, wetness index, and rainfall. Each study has a strong point and a weak point. Low-resolution satellite imagery has been used to assess drought vulnerability. At the island scale, it provides an overview of drought conditions, while at the provincial scale, it focuses on paddy fields and has little detailed information. In-situ measurements at the district scale detect meteorological drought accurately, but there were limitations in the mapping unit's detailed information. Drought mapping using GIS and remote sensing at the district scale has detailed spatial information on climate and physiographic aspects, but it needs temporal data monitoring.

A multidimensional analysis of marine capture fisheries in China’s coastal provinces

China (herein referred as China’s mainland, and excluding Hong Kong, Macau and Taiwan) is the largest contributor to global seafood production. While China’s marine fisheries have been extensively documented, there is a gap in systematically quantifying production of its marine fisheries and the different challenges confronting them at the provincial level. We addressed this gap in spatial detail by providing a review that compares and contrasts the exploitation history of China’s fisheries at both the national and provincial levels based on official statistical data. We expanded upon this to explore aspects of bio-socio-economic challenges faced by the country’s 11 fishing provinces. Our analysis suggested that significant increases in domestic marine catches in China have been accompanied by escalating fishing power, which has had differential impacts at the provincial scale. Catch per unit effort (CPUE) sharply declined at both the national and provincial scales, and many traditionally targeted demersal fish stocks showed clear downward trends in terms of catches. The 11 fishing provinces in China can be grouped into four clusters with distinct biological, social, and economic attributes. Targeted measures are recommended accordingly when implementing fisheries management measures for each specific fishing province in order to deliver an overall improvement in the sustainability of China’s marine fisheries.

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.

Prioritizing Flood-Prone Areas Using Spatial Data in the Province of New Brunswick, Canada

Over the years, floods have caused economic damage that has impacted development in many regions. As a result, a comprehensive overview of flood-prone areas at the provincial scale is important in order to identify zones that require detailed assessment with hydrodynamic models. This study presents two approaches that were used to prioritize flood-prone areas at the provincial scale in New Brunswick, Canada. The first approach is based on a spatial multi-criteria evaluation (SMCE) technique, while the second approach pertains to flood exposure analysis. The results show the variation in the identified flood-prone areas and, depending on the methodology and scenario used, prioritization changes. Therefore, a standard methodology might not be feasible and should be developed based on the objective of the study. The results obtained can be useful for flood risk practitioners when making decisions about where to commence detailed flood hazard and risk assessment.