Tao Zhang

Lakes are one of the most important parts of the terrestrial hydrosphere. The long-term series of lake area dynamic data with high spatial-temporal resolution is of great significance to the study of global change of the water environment. Satellite observations (such as Landsat) have provided images since the 1970s, but there were challenges for the construction of long-term sequences of lake area on a monthly temporal scale. We proposed a temporal-spatial interpolation and rule-based (TSIRB) approach on the Google Earth Engine, which aims to achieve automatic water extraction and bimonthly sequence construction of lake area. There are three main steps of this method which include bimonthly image sequence construction, automatic water extraction, and anomaly rectification. We applied the TSIRB method to five typical lakes (covering salt lakes, river lagoons, and plateau alpine lakes), and constructed the bimonthly surface water dataset (BSWD) from 1987 to 2020. The accuracy assessment that was based on a confusion matrix and random sampling showed that the average overall accuracy (OA) of water extraction was 96.6%, and the average Kappa was 0.90. The BSWD sequence was compared with the lake water level observation data, and the results show that the BSWD data is closely correlated with the water level observation sequence, with correlation coefficient greater than 0.87. The BSWD improves the hollows in the global surface water (GSW) monthly data and has advantages in the temporal continuity of surface water data. The BSWD can provide a 30-m-scale and bimonthly series of surface water for more than 30 years, which shows good value for the long-term dynamic monitoring of lakes, especially in areas that are lacking in situ surveying data.

In recent decades, the mangrove area in China has changed dramatically, and governments have established multiple mangrove protected areas at various levels. However, we know little about the effectiveness of conservation on mangroves on a national scale. In this study, we constructed an evaluation index system for landscape health and proposed a landscape health composite index (LHCI) to characterize the landscape health status of mangroves. Based on the distribution dataset of mangrove forests mangrove in the recent 40 years, we evaluated the conservation effectiveness of mangrove reserves in China from a perspective of landscape health. The dynamics of mangrove areas show that the mangrove area in 83% of the reserves increased after the establishment of reserves. Additionally, the increase in mangrove area in provincial-level, municipal-level, and county-level reserves was higher than that in national-level reserves, and the most significant increase in mangrove area was in Guangxi, followed by Fujian and Hong Kong. The evaluation results show that mangrove reserves have achieved outstanding conservation effectiveness in China, with 43% of the reserves significantly improving the landscape health status of mangroves and 35% of the reserves maintaining good condition. The reserves in Guangxi, Guangdong, and Fujian Provinces showed more significant protective effects. Specifically, the most effective reserves protecting mangroves were the Qi’ao Island reserve, Maowei Gulf reserve, and Enping reserve. This study may provide references for formulating a rapid evaluation method of conservation effectiveness based on remote sensing and promote the scientific management of protected areas and the ecological restoration of mangroves in China.

Reference evapotranspiration (ET₀) is a key component of hydrologic cycle and it is important for water resources management. Analysis of ET₀ changes is particularly critical for understanding the impacts of climatic change on hydrology in ecologically fragile regions. In this study, using the Penman–Monteith method and the Mann–Kendall test, the variation characteristics of ET₀ on the Tibetan Plateau (TP) from 1970 to 2018 was analyzed, and the dominant climatic factors controlling the change of ET₀ was also explored. The result shows that in TP region: (1) there was an abrupt change in the trend of ET₀ around 1997, and the ET₀ declined at a rate of −25.9 mm/decade during 1970–1996 but increased by 31.1 mm/decade during 1997–2018; (2) ET₀ is most sensitive to solar radiation, then relative humidity, wind speed and mean temperature; (3) the decrease of ET₀ before 1997 was mainly due to the decline of wind speed and the increase of relative humidity, while the increase of ET₀ after 1997 was mainly due to the decrease of relative humidity. The results of this study can provide data reference for the research of water balance on the TP.

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

As a developing country, China’s mangrove landscape pattern has undergone significant temporal and spatial changes over the last four decades. However, we know little about the changes in the mangrove landscape pattern characteristics other than the area at the national scale. The analysis of mangrove landscape pattern changes from different perspectives on a national scale can provide scientific support for mangrove protection and restoration. In this study, the temporal and spatial changes in the pattern of the mangrove landscape over the last 40 years in China were analyzed based on remote sensing data with high classification accuracy (99.3% of 2018). First, according to the natural geographical conditions of the coastal zone and the distribution of the mangroves, the distribution area of the mangroves in China was divided into 31 natural shores. Then, by selecting representative landscape indexes and constructing an integrated landscape index, the spatial-temporal changes in the landscape pattern of China’s mangroves over the last 40 years were analyzed based on five perspectives: Total area change, shape complexity, connectivity, fragmentation, and the integrated state of the landscape. From a temporal viewpoint, before 2000, the total area of each shore exhibited a downward trend, and the degree of connectivity deteriorated continuously, but the degree of fragmentation was stable at a low level. After 2000, although the total area improved, the degree of fragmentation gradually increased. The spatial changes are mainly reflected by the fact that the shores in Guangdong and Hainan exhibited significant differences within the same province. Based on the above analysis, corresponding scientific suggestions are proposed from temporal and spatial viewpoints to provide guidance for mangrove management and protection in China and to provide a reference for mangrove research in other regions of the world.

Mangrove forests are important ecosystems in the coastal intertidal zone, but China’s mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China’s mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning.

The change in land use during the process of urbanization affects surface runoff and increases flood risk in big cities. This study investigated the impact of land use change on surface runoff in Beijing’s central area during the period of rapid urbanization from 1984 to 2019. Land use maps of 1984, 1999, 2009, and 2019 were generated by image classification of Landsat images. Surface runoffs were calculated with the Soil Conservation Service curve number (SCS-CN) model. Correlation analysis was used to identify the dominant factor of land use change affecting surface runoff. The result showed that the variation trend of surface runoff was consistent with the trend of impervious land in Beijing’s central area, which increased during 1984~2009 and decreased during 2009~2019. Correlation analysis showed that changes in surface runoff were most strongly correlated with changes in impervious surfaces when compared with the correlation of runoff with other types of land use. The results of this study may provide a reference for city flood control and urban planning in fast growing cities worldwide, especially in developing countries.

Aquaculture coasts have become widely distributed in coastal zones as human activities are intensified. Due to the complexity in this type of coast, it is difficult to extract the coastline with traditional automated mapping approaches. In this paper, we present an automated method—object-based region growing integrating edge detection (OBRGIE) for the extraction of this type of coastline. In this method, a new object feature named OMI (object merging index) is proposed to separate land and sea. The OBRGIE method was applied to Landsat Thematic Mapper (TM) (pixel size 30m) and Satellite Pour l’Observation de la Terre (SPOT-5) (pixel size 10 m) images of two coastal segments with lengths of 272.7 km and 35.5 km respectively, and the accuracy of the extracted coastlines was assessed in comparison with the manually delineated coastlines. The mean and RMSE (root mean square error) are 16.0 m and 16.4 m respectively for the TM images, and 8.0 m and 8.6 m, respectively, for the SPOT-5 images, indicating that the proposed method derives coastlines with pixel accuracy. The OBRGIE method is also found to be robust to the segmentation scale parameter, and the OMI feature is much more effective than the spectral attribute in separating land and sea in aquaculture coasts. This method may provide an inexpensive means of fast coastline mapping from remotely sensed imagery with relatively fine-to-moderate spatial resolution in coastal sectors with intense human interference.

As manually correcting error in visual interpretation data is both costly and labor intensive, the automated error detection and correction approaches is in need. In this paper, a prototype method is developed to detect and correct errors in visually interpreted landuse data. This method involves image segmentation, anomaly detection and decision tree classification techniques. The method is tested on landuse dataset with known accuracy (95%-50%). Result shows that the accuracy of landuse data can be greatly improved by the error correction method, and the approach can be practical when the accuracy of the interpretation data is no less than 70%.