Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products

Nature contact and general health: testing multiple serial mediation pathways with data from adults in 18 countries

This is the final version. Available on open access from Elsevier via the DOI in this record. Data availability: A subset of the data is available at: (Elliott, LR, White, MP. 2022. BlueHealth International Survey Dataset, 2017-2018. [data collection]. UK Data Service. SN: 8874, doi: 10.5255/UKDA-SN-8874-2).The role of neighbourhood nature in promoting good health is increasingly recognised in policy and practice, but consistent evidence for the underlying mechanisms is lacking. Heterogeneity in exposure methods, outcome measures, and population characteristics, little exploration of recreational use or the role of different types of green or blue space, and multiple separate mediation models in previous studies have limited our ability to synthesise findings and draw clear conclusions. We examined multiple pathways linking different types of neighbourhood nature with general health using a harmonised international sample of adults. Using cross-sectional survey data from 18 countries (n = 15,917), we developed a multigroup path model to test theorised pathways, controlling for sociodemographic variables. We tested the possibility that neighbourhood nature (e.g. greenspace, inland bluespace, and coastal bluespace) would be associated with general health through lower air pollution exposure, greater physical activity attainment, more social contact, and higher subjective well-being. However, our central prediction was that associations between different types of neighbourhood nature and general health would largely be serially mediated by recent visit frequency to corresponding environment types, and, subsequently, physical activity, social contact, and subjective well-being associated with these frequencies. Several subsidiary analyses assessed the robustness of the results to alternative model specifications as well as effect modification by sociodemographics. Consistent with this prediction, there was statistical support for eight of nine potential serial mediation pathways via visit frequency which held for a range of alternative model specifications. Effect modification by financial strain, sex, age, and urbanicity altered some associations but did not necessarily support the idea that nature reduced health inequalities. The results demonstrate that across countries, theorised nature-health linkages operate primarily through recreational contact with natural environments. This provides arguments for greater efforts to support use of local green/blue spaces for health promotion and disease prevention.European Union Horizon 202

Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications To Human Settlement Modelling

Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.</p

Time Series Analysis of Long-Term Vegetation Trends, Phenology, and Ecosystem Service Valuation for Grasslands in the U.S. Great Plains

Doctor of PhilosophyDepartment of GeographyJ. M. Shawn HutchinsonGrasslands are one of the largest, most biodiverse, and productive terrestrial biomes but they receive very low levels of protection. The temperate grasslands in the United States are one of the most threatened grassland ecosystems. Every year, a significant portion of grasslands in the Great Plains are converted to agricultural use, with almost 96% of the historical extent lost. Other factors that affect existing grassland health include significant climatic changes, invasion of woody, non-native species, fragmentation, lack or inadequate burning, and excessive grazing. The impact of the combination of these factors on grasslands in the US Great Plains is still unknown. The goal of this research is to investigate the long-term grassland vegetation conditions using a well-known indicator (greenness) and assesses its impact on the provision of select grassland ecosystem services within the US Great Plains from 2001 to 2017. The above goal was achieved with three objectives addressed in three chapters. In Chapter 3, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 6) was performed to assess long-term trends in vegetation greenness across the Great Plains ecoregion of the United States. The Breaks for Additive Season and Trend (BFAST) decomposition method was applied to a time series of images from 2001 to 2017 to derive spatially explicit estimates of gradual interannual change. Results show more 'greening' trends than 'browning' and 'no change' trends during the study period. Comparing the trend results from both vegetation indices suggests that EVI is more suitable for this analysis in the study area, especially in areas with high biomass. In Chapter 4, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 5) is used to explore spatial patterns of vegetation phenology and to assess long-term phenology trends across the region. The program TIMESAT was used to extract key measures of vegetation phenological development from 2001 to 2017, including the phenometrics (1) season length, (2) start of growing season, (3) end of growing season, (4) middle of the growing season, (5) maximum NDVI value, (6) small integral, (7) left derivative, and (8) right derivative. Results show important variation in phenological patterns across the region such as a shift to a later start, earlier end, and shorter the growing season length, especially in the southern parts of the region. As shown in the small integral and maximum EVI, vegetation productivity appears to have increased over many areas in the Great Plains ecoregion. Finally, Chapter 5 focuses on developing a methodological improvement to the widely used Invest ecosystem services model that uses remotely sensed inputs to capture the interannual spatio-temporal dynamics of grassland vegetation on the provision of grassland ecosystem services across the US Great Plains. A selected set of grassland ecosystem services was quantified (economic and biophysical values) for the period between 2001 and 2017. This exploratory study will be a basis for highlighting the role grasslands play in providing essential ecosystem services and how improved long-term vegetation monitoring can benefit land-use decisions locally and regionally