Agricultural drought in the Vietnamese Central Highlands at 1-km resolution: Monthly and annual datasets.

Drought is a complex natural hazard which can create significant impacts on society and environment. Given that this phenomenon varies across space and changes over time dependent on various factors (e.g., physical conditions and human activities), the available of spatiotemporal drought data enables a better monitoring and assessment of drought severity This study introduced the integrated multivariate drought index (iMDI) data, a new regional drought index, at 1 km spatial and monthly temporal resolutions for the Vietnamese Central Highlands over a 20-years period. The iMDI was developed recently which is a combination of vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI) based on the feature of scaling algorithms (i.e., normalisations and standardisation). The data were processed using the median values of MODIS time-series imagery obtained from the Google Earth Engine (GEE) platform. The iMDI datasets are available for monthly and annual drought monitoring between 2001 and 2020. Additionally, the datasets of VCI, TCI, and ESI were provided so that users can apply for their own purposes even though these data can directly obtain from GEE or other sources. Users, especially those without technical expertise, can reap the advantages of having open access to iDMI data. By doing so, they can reduce their expenses and the time required to process data. As such, this accessibility can promote the use of data for diverse applications, such as evaluating the impact of droughts on the environment and human activities and monitoring droughts regionally.

Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country.

Knowing how landscape structure affects the provision of ecosystem services (ES) is an important first step toward better landscape planning. Because landscape structure is often heterogenous across space, modelling the relationship between landscape structure and the provision of ES must account for spatial non-stationarity. This paper examines the relationship between landscape structure and the provision of ES using a hill country and steep-land case farm in New Zealand. Indicators derived from land cover and topographical data such as Largest Patch Index (LPI), Contrast Class Edge (CCE), Edge Density (ED), and Terrain slope (SLOPE) were used to examine the landscape's structure and pattern. Measures of pasture productivity, soil erosion control, and water supply were derived with InVEST tools and spatial analysis in a GIS. Multiscale Geographically Weighted Regression (MGWR) was used to evaluate the relationship between indicators of landscape structure and the provisioning of ES. Other regression models, including Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR), were carried out to evaluate the performance of MGWR. Results showed that landscape patterns significantly affect the supply of all mapped ES, and this varies across the landscape, dependent on the pattern of topographical features and land cover pattern and structure. MWGR outperformed other OLS and GWR in terms of explanatory power of the ES determinants and had a better ability to deal with the presence of spatial autocorrelation. Spatially and quantitatively detailed variations of the relationship between landscape structure and the provision of ES provide a scientific basis to inform the design of sustainable multifunctional landscapes. Information derived from this analysis can be used for spatial planning of farmed landscapes to promote multiple ES which meet multiple sustainable development objectives.

Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach.

Most coastal areas globally face water shortages in the dry season due to salinization and drought. The Mekong River Delta (MRD) is recognized as the "Rice Bowl" in Vietnam but the negative effects of salinization and drought have damaged rice production in recent decades. However, regional assessment of the perturbation has been lacking. A Landsat-based satellite salinity index, the Enhanced Salinity Index (ESI), was developed in this study to explore patterns of annual salinity variations in agricultural land and their relationship to drought in the MRD from 1989 to 2018. The performance of the index was superior to that of other previously published remotely sensed indices, based on correlations with field measurements of electrical conductivity (i.e. groundwater and soil EC), which can be used as a proxy for salinity. The time-series ESI was then utilized to explore the spatiotemporal dynamics of salinity in the study area using the Theil-Send median trend (TS) and Mann-Kendall significance tests (MK). In addition, temporal relationships with the Normalized Difference Water Index (NDWI) were used to investigate the relationship between drought and saline intrusion. Our results showed that freshwater and brackish areas increased inland, whereas those developed for shrimp farming may increase soil and groundwater salinity. A negative correlation between drought and salinity was also observed in surface water where fish and shrimp farming activities took place, while a positive relationship was discovered in rice and annual cropland areas. This study highlights the use of ESI as an effective parameter for modelling vegetation salinity and its relationship with cropland change. We also demonstrate the feasibility of integrating satellite imagery with spatiotemporal analyses to monitor and assess regional salinization dynamics.