It has been frequently stated, but without provision of supporting evidence, that the world has lost 50% of its wetlands (or 50% since 1900 AD). This review of 189 reports of change in wetland area finds that the reported long-term loss of natural wetlands averages between 54–57% but loss may have been as high as 87% since 1700 AD. There has been a much (3.7 times) faster rate of wetland loss during the 20th and early 21st centuries, with a loss of 64–71% of wetlands since 1900 AD. Losses have been larger and faster for inland than coastal natural wetlands. Although the rate of wetland loss in Europe has slowed, and in North America has remained low since the 1980s, the rate has remained high in Asia, where large-scale and rapid conversion of coastal and inland natural wetlands is continuing. It is unclear whether the investment by national governments in the Ramsar Convention on Wetlands has influenced these rates of loss. There is a need to improve the knowledge of change in wetland areas worldwide, particularly for Africa, the Neotropics and Oceania, and to improve the consistency of data on change in wetland areas in published papers and reports.

How much wetland has the world lost? Long-term and recent trends in global wetland area

Global wetlands: Potential distribution, wetland loss, and status

Regional differences of lake evolution across China during 1960s–2015 and its natural and anthropogenic causes

Despite their value, coastal ecosystems are globally threatened by anthropogenic impacts, yet how these impacts are driven by economic development is not well understood. We compiled a multifaceted dataset to quantify coastal trends and examine the role of economic growth in China’s coastal degradation since the 1950s. Although China’s coastal population growth did not change following the 1978 economic reforms, its coastal economy increased by orders of magnitude. All 15 coastal human impacts examined increased over time, especially after the reforms. Econometric analysis revealed positive relationships between most impacts and GDP across temporal and spatial scales, often lacking dropping thresholds. These relationships generally held when influences of population growth were addressed by analyzing per capita impacts, and when population density was included as explanatory variables. Historical trends in physical and biotic indicators showed that China’s coastal ecosystems changed little or slowly between the 1950s and 1978, but have degraded at accelerated rates since 1978. Thus economic growth has been the cause of accelerating human damage to China’s coastal ecosystems. China’s GDP per capita remains very low. Without strict conservation efforts, continuing economic growth will further degrade China’s coastal ecosystems.

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Economic development and coastal ecosystem change in China

China's wetlands loss to urban expansion

Four wetland maps for all China have been produced, based on Landsat and CBERS-02B remote sensing data between 1978 and 2008 (1978, 1990, 2000 and 2008). These maps were mainly developed by manual interpretation and validated by substantial field investigation in 2009. Based on these maps, we analyzed the 2008 wetland distribution in China and discussed wetland changes and their drivers over the past 30 years. (i) There were about 324097 km(2) of wetlands in 2008, for which inland marshes or swamps were the most common wetland type (35%), with lakes (26%) second. Most of the wetlands were in Heilongjiang, Inner Mongolia, Qinghai and Tibet, occupying about 55% of the national wetland area. (ii) From 1978 to 2008, China's wetland area continually and significantly decreased, by about 33% based on changes in the wetland map. This was in sharp contrast to the increase in artificial wetlands, which increased by about 122%. Inland marshes accounted for the main loss of total wetlands from 1978 to 2000. From 2000 through 2008, riverine and lacustrine wetlands constituted the main wetland loss. Fortunately however, the rate of wetland loss decreased from 5523 to 831 km(2)/a. (iii) The change ratio of lost natural wetlands (including inland and coastal wetlands) to non-wetlands has decreased slightly over the past 30 years. From 1978 to 1990, nearly all natural wetlands (98%) lost were transformed into non-wetlands. However, the ratio declined to 86% from 1990 to 2000, and to 77% from 2000 to 2008. (iv) All Chinese provinces were divided into three groups according to patterns of wetland changes, which could relate to the driving forces of such changes. Tibet was completely different from other provinces, as it was one representative example in which there was a net wetland increase, because of global warming and decreased human activity since 1990. Increased economic development caused considerable wetland loss in most eastern provinces, and artificial wetlands increased.

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Mapping wetland changes in China between 1978 and 2008

The Huangshui River Basin is one of the most densely populated areas on the Qinghai–Tibet Plateau and is characterized by a high level of human activity. The contradiction between ecological protection and socioeconomic development has become increasingly prominent; determining how to achieve the balanced and coordinated development of the Huangshui River Basin is an important task. Thus, this study used the Google Earth Engine (GEE) cloud-computing platform and Sentinel-1/2 data, supplemented with an ALOS digital elevation model (ALOS DEM) and field survey data, and combined a remote sensing classification method, grid method, and ecosystem service value (ESV) evaluation method to study the spatial correlation and interaction between land use (LU) and ESV in the Huangshui River Basin. The following results were obtained: (1) on the GEE platform, Sentinel-1/2 active and passive remote sensing data, combined with the gradient tree-boosting algorithm, can efficiently produce highly accurate LU data with a spatial resolution of 10 m in the Huangshui River Basin; the overall accuracy (OA) reached 88%. (2) The total ESV in the Huangshui River Basin in 2020 was CNY 33.18 billion (USD 4867.2 million), of which woodland and grassland were the main contributors to ESV. In the Huangshui River Basin, the LU type, LU degree, and ESV have significant positive spatial correlations, with urban and agricultural areas showing an H-H agglomeration in terms of LU degree, with woodlands, grasslands, reservoirs, and wetlands showing an H-H agglomeration in terms of ESV. (3) There is a significant negative spatial correlation between the LU degree and ESV in the Huangshui River Basin, indicating that the enhancement of the LU degree in the basin could have a negative spatial spillover effect on the ESV of surrounding areas. Thus, green development should be the future direction of progress in the Huangshui River Basin, i.e., while maintaining and expanding the land for ecological protection and restoration, and the LU structure should be actively adjusted to ensure ecological security and coordinated and sustainable socioeconomic development in the Basin.

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Spatial Autocorrelation Analysis of Land Use and Ecosystem Service Value in the Huangshui River Basin at the Grid Scale

The present study aims to quantitatively assess soil erosion intensity (SEI) and amounts in the Qinghai Lake Basin (QLB) over the 1990–2020 period using the Integrated Valuation Ecosystem Services and Tradeoffs (InVEST) model based on multi-source data. In addition, the changing trends and driving factors of soil erosion (SE) in the study area were systematically analyzed. The result showed: (1) An increasing-decreasing trend in the total soil erosion amount (SEA) in the QLB over the 1990–2020 period, with an average SEI of 579.52 t/km2. In addition, very low and low erosion classes covered 94.49% of the total surface area, while areas with high SEI were mainly distributed in alpine areas with low vegetation coverage (VC). (2) The highest average SEI was observed in bare land, while grassland and unused land were the main land use (LU) types where SE mainly occurred, with the ratio of the two being 95.78%. (3) The average value of SEI was positively correlated with altitude values below 4800 m. In addition, areas with altitude ranges of 4000–4400 m, 3600–4000 m, and 4400–4800 m were the main areas where SE occurred, with an average total soil erosion ratio (SER) value of 88.73%. (4) The average SEI was directly proportional to the slope degrees. SE occurred mainly in the areas with slope degree ranges of 15–25°, 25–35°, 8–15°, and >35°, accounting for 93.16% of the average total SER value. (5) The q value of the two-factor interaction was greater than that of the single-factor interaction. In addition, the areas with a high SE risk were mainly those with 1220–2510 m rainfall, <0.104 VC, the land use/land cover (LULC) type bare land, the altitude range 4400–4800 m, and a slope of >35°. The interaction between rainfall, VC, LULC, elevation, and slope had a significant impact on the spatial distribution of SEI.

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Spatiotemporal Variation of Soil Erosion Characteristics in the Qinghai Lake Basin Based on the InVEST Model

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2, and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increased the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects, and landscape ecology uncertainty studies.

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XiaoHong Gao

Papers

Mapping wetland changes in China between 1978 and 2008

Spatial Autocorrelation Analysis of Land Use and Ecosystem Service Value in the Huangshui River Basin at the Grid Scale

Spatiotemporal Variation of Soil Erosion Characteristics in the Qinghai Lake Basin Based on the InVEST Model

Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data