remote sensing Article Wetland Loss Identification and Evaluation Based on Landscape and Remote Sensing Indices in Xiong’an New Area Jinxia Lv 1,2 , Weiguo Jiang 1,2,* , Wenjie Wang 3, Zhifeng Wu 4, Yinghui Liu 1, Xiaoya Wang 1,2 and Zhuo Li 1,2 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; [email protected] (J.L.); [email protected] (Y.L.); [email protected] (X.W.); [email protected] (Z.L.) 2 State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 3 Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] 4 School of Geographical Science, Guangzhou University, Guangzhou 510006, China; [email protected] * Correspondence: [email protected] Received: 5 October 2019; Accepted: 26 November 2019; Published: 29 November 2019 Abstract: Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong’an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km2, with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km2 in the Xiong’an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km2, 20.71 km2 and 80.05 km2 by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas. Keywords: wetland; landscape variation; remote sensing indices; Xiong’an New Area Remote Sens. 2019, 11, 2834; doi:10.3390/rs11232834 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 2834 2 of 21 1. Introduction Wetlands, formed in the transitional zone between land and water, are widely distributed worldwide [1]. These critical parts of ecosystems serve many essential functions to people and the environment, including supporting many wild animals and plant resources and providing flood protection, a water supply, erosion control and climate regulation [2–4]. However, with the development of urbanization and the population growth, global wetland areas have degraded or even disappeared [5], and the total wetland area in China is the fourth largest area of any country [6]. With the demand for ecological civilization, the wetland loss has received much attention from scientists. Many researchers have attempted to identify wetland degradation. Current wetland degradation studies include wetland area degradation [7–9], wetland elements variation [10–12] and wetland ecosystem services [13]. The amount and pattern of wetland loss in China was analyzed from 1990 to 2010 based on land cover data [7]. The results showed that during these 20 years, China lost 2883 km2 of wetlands to urban expansion. Other researchers have reached similar conclusions. For instance, using Landsat images, wetland regions were extracted, which showed that China has experienced a net loss of 50,360 km2 from 1990 to 2000 [8]. In addition, using Landsat images, China’s wetlands loss was detected by the land use matrix and the results proposed that China’s total wetland area decreased by 101,399 km2 from 1978 to 2008 [9]. These studies mainly used the land use data to analyze the wetland loss and were concentrated on separate periods. They showed some deficiency in temporal continuity. Due to the short-term nature of land use data availability, some researchers have analyzed wetland degradation based on the wetland elements by using long-term remote sensing data series. The critical wetland elements were water [10,14], vegetation [15,16] and soil moisture [14,17]. The water area variation of Wuhan urban agglomeration Lake was monitored with Landsat images [10]. The playa wetland condition was evaluated using four types of spectral water indices derived from Landsat images and the Google Earth Engine [14]. Furthermore, the wetland vegetation and soil moisture conditions were generally represented with the normalized difference vegetation index (NDVI) and the soil index to identify wetland change. For instance, the wetland vegetation was monitored in the Danshui River estuary in Taipei using the NDVI imagery produced from SPOT to monitor [18]. The Great Artesian Basin wetland vegetation was analyzed by using MODIS NDVI, and the results show that the vegetated wetland area declined from 2002 to 2009 [17]. The Biebrza Wetland soil moisture conditions were evaluated with the help of Sentinel-1 data [12]. Previous studies on wetland area analysis have focused on the wetland classes and have ignored the wetland area variation of the same class. Furthermore, studies of elements have concentrated on the evolution of a single element and have lacked a synthetic analysis. Therefore, it is necessary to establish a new method to identify the process of wetland loss by integrating the landscape with the elements succession process. In China, there are many types of wetlands, including plateau, lake, coastal and urban wetlands [9]. Baiyangdian Lake, the largest natural freshwater wetland in the semi-arid North China Plain, is known as the “Pearl of North China” and the “Kidney of North China” and will be an important city wetland in the future of the Xiong’an New Area. On 1 April 2017, China’s Central Committee and State Council announced a plan to construct the Xiong’an New Area to improve the functioning of the non-capital in Beijing. In the future, the region will become an international metropolis. However, with the impact of climate change and human activities, the wetlands have suffered severe droughts and the wetland area has declined. Many efforts have been undertaken to identify the degradation and condition of wetlands [19–21]. For instance, it was analyzed that the long-term change in surface water area in the Xiong’an New Area using dense Landsat time series images, which indicated that the wetland has degraded and that this degradation was driven by human activities [20]. The ecological status of areas within the Xiong’an New Area was analyzed by proposing an ecological index [21]. This study is expected to build a new method for identifying wetland loss in the Xiong’an New Area and to shed light on wetland loss and future development. The Xiong’an New Area was selected as the study area, with the purpose of proposing a new method for wetland loss identification. As increasing quantities of satellite data are becoming available, Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 21 The Xiong’an New Area was selected as the study area, with the purpose of proposing a new method for wetland loss identification. As increasing quantities of satellite data are becoming available, remote sensing data shows great importance in wetland field [22,23]. This study used Landsat time series images and seven land use datasets from 1989 to 2015 to analyze the wetland loss by the land cover transition matrix and trend analysis methods. Therefore, the specific objectives were (1) to analyze the wetland landscape variation and loss in the entire region and to calculate the maximum wetland area; (2) to evaluate the wetland elements loss in maximum wetland extent by sub-regions and (3) to summarize the pattern of wetland loss in the integrated landscape and elementsRemote Sens. and2019 present, 11, 2834 a case study in wetland loss identification. 3 of 21 2. Materials remote sensing data shows great importance in wetland field [22,23]. This study used Landsat time 2.1.series Study images Area and seven land use datasets from 1989 to 2015 to analyze the wetland loss by the land cover transition matrix and trend analysis methods. Therefore, the specific objectives were (1) to The Xiong’an New Area has a sub-frigid and sub-humid climate, with an average annual analyze the wetland landscape variation and loss in the entire region and to calculate the maximum temperature of approximately 12 °C.