A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.

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.

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parameters—accessibility, multi natural hazard, biohazard, comfort index, and land value—to determine potential areas for new schools to achieve educational equity targets.

Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement.

Monitoring Cropping Intensity Dynamics across the North China Plain from 1982 to 2018 Using GLASS LAI Products

China is a large grain producer and consumer. Thus, obtaining information about the cropping intensity (CI) in cultivated land, as well as understanding the intensified utilization of cultivated land, is important to ensuring an increased grain production and food security for China. This study aims to detect and map the changes in CI over a period of 36 years across China’s core grain-producing area—the North China Plain (NCP)— using remotely sensed leaf area index (LAI) time series data acquired by the Global LAnd Surface Satellite (GLASS) products. We first selected 2132 sample points that consisted entirely, or almost entirely, of cultivated cropland from all pixels; the biennial LAI curves for the sample points were then extracted; the Savitzky–Golay filter and second-order difference algorithm were then applied to reconstruct the biennial LAI curves and obtain the number of peaks in these curves. In addition, the multiple cropping index (MCI) was calculated to represent the CI. Finally, the spatial distribution of the CI of cultivated land on the NCP was mapped from 1982 to 2018 using a geo-statistical kriging approach. Spatially, the results indicate that the CI of cultivated land over the NCP exhibits a distinct spatial pattern that conforms to “high in the south, low in the north”. The single cropping system (SCS) mainly occurred in the higher latitude area ranging from 37.04°N to 42.54°N, and the double cropping system (DCS) mainly existed in the lower latitude area between 31.95°N and 39.97°N. Temporally, the CI increased over the study period, but there were some large fluctuations in CI from 1982 to 1998 and it maintained relatively stable since 2000. Across the NCP, 68.14% of cultivated land experienced a significant increase in CI during the 36-year period, while only 3.87% showed a significant decrease. We also found that, between 1982 and 2018, the northern boundary of the area for DCS underwent a significant westward expansion and northward movement. Our results show a good degree of consistency with statistical data and previous research and also help to improve the reliability of satellite-based identification of CI using low spatial resolution LAI products. The results provide important information that can be used for analyzing and evaluating the rational utilization of cultivated land resources; thus, ensuring food security and realizing agricultural sustainability not only for the NCP, but for China as a whole. These results also highlight the value of satellite remote sensing to the long-term monitoring of cropping intensity at large scales.

Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia

The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world’s mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the “very suitable” and “suitable” class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a “very suitable” class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems.

Multi-Source Remote Sensing Data Product Analysis: Investigating Anthropogenic and Naturogenic Impacts on Mangroves in Southeast Asia

This study investigated the drivers of degradation in Southeast Asian mangroves through multi-source remote sensing data products. The degradation drivers that affect approximately half of this area are unidentified; therefore, naturogenic and anthropogenic impacts on these mangroves were studied. Various global land cover (GLC) products were harmonized and examined to identify major anthropogenic changes affecting mangrove habitats. To investigate the naturogenic factors, the impact of the water balance was evaluated using the Normalized Difference Vegetation Index (NDVI), and evapotranspiration and precipitation data. Vegetation indices’ response in deforested mangrove regions depends significantly on the type of drivers. A trend analysis and break point detection of percentage of tree cover (PTC), percentage of non-tree vegetation (PNTV), and percentage of non-vegetation (PNV) datasets can aid in measuring, estimating, and tracing the drivers of change. The assimilation of GLC products suggests that agriculture and fisheries are the predominant drivers of mangrove degradation. The relationship between water balance and degradation shows that naturogenic drivers have a wider impact than anthropogenic drivers, and degradation in particular regions is likely to be a result of the accumulation of various drivers. In large-scale studies, remote sensing data products could be integrated as a remarkably powerful instrument in assisting evidence-based policy making.