The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.

Guilin City, located in a typical karst landform area in China, is one of the sustainable development demonstration zones. Evaluating the habitat quality of Guilin City and exploring its driving factors are helpful in formulating effective measures for sustainable development. Based on the Integrated Valuation of Ecosystem Servicesand Tradeoffs (InVEST) model and combined methods such as spatial autocorrelation analysis, Geographical detector model and Geographically weighted regression (GWR) model, this study evaluated the habitat quality of Guilin City from 2001 to 2022. The study also analyzed the spatiotemporal characteristics and their possible driving factors. The results indicate that: (1) The average habitat quality in Guilin City was 0.59, with 47.98% of the area classified as having good or excellent habitat quality; however, habitat quality has shown a downward trend over the past 22 years. (2) Moran’s I values for habitat quality in Guilin City were all greater than 0.8, indicating a significant positive spatial correlation and spatial clustering. Among these, the low–low aggregation regions were the largest, whereas the high–high aggregation regions showed the most significant decrease. (3) Elevation was the most significant factor affecting the spatial differentiation of habitat quality in Guilin. The interactions between various driving factors were stronger than those between any single factor, with most interactions exhibiting a dual-factor enhancement effect. This study highlights the complexity of the comprehensive impact of multiple factors on habitat quality changes and provides a scientific basis and policy recommendations for ecological protection within the national sustainable development agenda’s innovative demonstration zones.

The 17 Sustainable Development Goals (SDGs) were proposed in 2030 Agenda for Sustainable Development of the United Nations in 2015, in which SDG 3 is about ensuring healthy lives and promoting well-being for all ages. The assessment on SDG 3 helps to measure or monitor the level of sustainable and healthy development of nation or sub-nation regions. However, the SDG 3 is mainly aimed at the global or national levels, and lack of quantitative assessment and analysis. Therefore, this paper, taking Deqing County of Zhejiang Province in China as an example, presents a pilot study which measured county-level SDG 3 by using statistical information and geospatial information. There are three main steps: Firstly, a set of localized indicators for SDG 3 was built according to adaptability, scalability, and coverage, combined with the specific conditions of Deqing County. Secondly, the selected indicators were calculated by combining integration of statistical and geospatial information. Thirdly, an assessment based on dashboard was processed. The results showed that 12 of the 15 health-related indicators in Deqing County were evaluated as green, 1 was yellow, and 2 were gray. The morbidity rate and mortality rate of infectious diseases in 2010–2017 are low and essential health-care services are equally covered in space. This study provides scientific basis for the management of healthy and sustainable development in Deqing County, and provides a reference example for monitoring and evaluation of SDG 3 in other regions.