Observed inequality in urban greenspace exposure in China.

Change detection is a vibrant area of research in remote sensing. Thanks to increases in the spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can now be effectively detected. However, change detection from very-high-spatial-resolution (VHR) (≤5 m) remote sensing images is challenging due to limited spectral information, spectral variability, geometric distortion, and information loss. To address these challenges, many change detection algorithms have been developed. However, a comprehensive review of change detection in VHR images is lacking in the existing literature. This review aims to fill the gap and mainly includes three aspects: methods, applications, and future directions.

Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a “crowdsourcing-change detection-classification-expert knowledge” vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.

An updated Vegetation Map of China (1:1000000)

Characterized by intensive urban sprawl and continuous cropland shrinkage, the unprecedented urbanization process has profoundly reshaped China’s landscape over the past four decades. However, the interaction between urban expansion and cropland loss in China at a finer spatiotemporal resolution remains unclear. This study aims to quantify and compare the rates, patterns, dynamics, and interactions of urban expansion and cropland loss in 14 Chinese cities during 1980–2015. Multiple landscape metrics were calculated to quantify the magnitudes, rates, and patterns of urban expansion and cropland loss for each city. The standard deviation ellipse analysis and two quantitative indices (the dependence and the contribution of urban expansion on cropland loss) were used to characterize the relationship between urban expansion and cropland loss. The pattern of rapid urban expansion and extensive cropland loss was observed across all selected cities (except for Harbin), with the averaged expansion area of 764.17 km2 and averaged loss area of 650.83 km2 per city. The primary mode of urbanization was the edge-expansion (6889.22 km2, 60.01%), followed by the infilling (2767.32 km2, 24,11%) and the outlying (1822.72 km2, 15.88%). Urban expansion was identified to be the dominant driver of cropland loss, accounting for 84.99% of the newly expanded urban land and 74.36% of the lost cropland in total, thus leading to a more spatially irregular and fragmented distribution of the cropland. The balance between urbanization and land protection is still challenging. Here we advocate more effective policy-driven practices to protect China’s existing cropland for food security and sustainable development goals.

How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015

Microplastics are being widely discussed as an emerging global environmental contaminant. Microplastic pollution usually originates from land-based sources, which are then mainly transported through hydrological and atmospheric pathways and accumulated in terrestrial, freshwater and marine ecosystems. Urban environments represent a condensed area of human activities (including the production and use of plastic materials), and urban rivers may therefore be a key transporter of microplastic pollution. Understanding microplastic abundances in urban rivers is potentially important in finding effective means of reducing fluvial microplastic discharge. This study quantified microplastic abundances in surface waters along the Fenghua River, Ningbo, a coastal megacity in East China. Microplastic pollution was distributed unevenly along the river, with concentrations ranging from 300 n/m3 to 4000 n/m3 (0.3 – 4.0 n/L). Average concenterations were 1620.16 ± 878.22 n/m3 (1.62 ± 0.88 n/L) in summer (43 sampling points) and 1696.08 ± 983.52 n/m3 (1.70 ± 0.98 n/L) in winter (17 sampling points). The most common microplastic shapes, sizes, colors and types of polymers were fiber,

Microplastic pollution in Chinese urban rivers: The influence of urban factors

Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare.

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

A heavy workload is required for sample collection for urban land use classification, and researchers are in urgent need of sampling strategies as a guide to achieve more effective work. In this paper, we make use of an urban land use survey to obtain a complete sample set of a city, test the impact of different training and validation sample sizes on the accuracy, and summarize the sampling strategy. The following conclusions are drawn based on our systematic analysis in Shenzhen. (1) For the best classification accuracy, the number of training samples should be no less than 40% of the total number of parcels or no less than 5500 parcels. For the best labor cost performance, the number should be no less than 7% or no less than 900. (2) The accuracy evaluation is stable and reliable and requires validation sample numbers of no less than 10% of the total or no less than 1200. (3) Samples with a purity of 60–90% are preferred, and the classification effectiveness is better in samples with a purity greater than 90% under the same number. (4) If spatial equilibrium sampling cannot be carried out, sampling areas with complex land use patterns should be preferred.

Mo Su

Papers

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen