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Tianjin is the largest open city along the coastline in Northern China, which has several important wetland ecosystems. However, no systematic study has assessed the water body changes over the past few decades for Tianjin, not to mention their response to human activities and climate change. Here, based on the water change tracking (WCT) algorithm, we proposed an improved water change tracking (IWCT) algorithm, which could remove built-up shade noise (account for 0.4%~6.0% of the final water area) and correct omitted water pixels (account for 1.1%~5.1% of the final water area) by taking the time-series data into consideration. The seasonal water product of the Global Surface Water Data (GSWD) was used to provide a comparison with the IWCT results. Significant changes in water bodies of the selected area in Tianjin were revealed from the time-series water maps. The permanent water area of Tianjin decreased 282.5 km2 from 1984 to 2019. Each time after the dried-up period, due to government policies, the land reclamation happened in Tuanbo Birds Nature Reserve (TBNR), and, finally, 12.6 km2 of the lake has been reclaimed. Meanwhile, 488.6 km2 of land has been reclaimed from the sea along the coastal zone in the past 16 years at a speed of 28.74 km2 yr−1 in the Binhai New Area (BHNA). The method developed in this study could be extended to other sensors which have similar band settings with Landsat; the products acquired in this study could provide fundamental reference for the wetland management in Tianjin.
Xingxing Han; Wei Chen; Bo Ping; Yong Hu. Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data. Remote Sensing 2021, 13, 493 .
AMA StyleXingxing Han, Wei Chen, Bo Ping, Yong Hu. Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data. Remote Sensing. 2021; 13 (3):493.
Chicago/Turabian StyleXingxing Han; Wei Chen; Bo Ping; Yong Hu. 2021. "Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data." Remote Sensing 13, no. 3: 493.
Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS and WA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio (PSNR) and structural similarity index (SSIM) in ECS, NWP and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SR performance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy.
Bo Ping; Fenzhen Su; Xingxing Han; Yunshan Meng. Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 887 -896.
AMA StyleBo Ping, Fenzhen Su, Xingxing Han, Yunshan Meng. Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):887-896.
Chicago/Turabian StyleBo Ping; Fenzhen Su; Xingxing Han; Yunshan Meng. 2020. "Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 887-896.
As China’s largest freshwater lake and an important wintering ground for white cranes in Asia, the Poyang Lake wetland has unique ecological value. However, wetland cover types have changed dynamically and have attracted the attention of society and researchers over the past few decades. To obtain detailed knowledge and understanding of the long-term landcover dynamics of Poyang Lake and the associated driving forces, Landsat and HJ-1A/B images (31 images) were used to acquire classification and frequency maps of Poyang Lake in the dry season from 1973–2019 based on the random forest (RF) algorithm. In addition, the driving forces were discussed according to the Geodetector model. The results showed that the coverage of water and mudflat showed opposite trends from 1987–2019. Water and vegetation exhibited a significant decreasing trend from 1981–2003 and from 1996–2004 (p < 0.01), respectively. A phenomenon of vegetation expanding from west to east was found, and the expansion areas were mainly concentrated in the central zone of Poyang Lake, while vegetation in the northern mountainous area of Songmen (region 1) and eastern Songmen Mountain (region 2), showed a significantly expanded trend (R2 > 0.6, p < 0.01) during the five-decade period. The year-long dominant distribution of water occurred mainly in the two deltas formed by the Raohe and Tongjin rivers and the Fuhe and Xinjiang rivers, with deep water. In the 1973–2003 and 2003–2019 periods, a total of 313.522 km2 of water turned into swamp and mudflat and 478.453 km2 of swamp and mudflat transitioned into vegetation, respectively. Elevation and temperature appeared to be the main factors affecting the regional wetland evolution in the dry season and should be considered in the management of Poyang Lake. The findings of this work provide detailed information for spatial–temporal landcover changes of Poyang Lake, which could help policymakers to formulate scientific and appropriate policies and achieve restoration of the Poyang Lake wetland.
Sa Wang; Lifu Zhang; Hongming Zhang; Xingxing Han; Linshan Zhang. Spatial–Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A/B Data in the Dry Season from 1973–2019. Remote Sensing 2020, 12, 1595 .
AMA StyleSa Wang, Lifu Zhang, Hongming Zhang, Xingxing Han, Linshan Zhang. Spatial–Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A/B Data in the Dry Season from 1973–2019. Remote Sensing. 2020; 12 (10):1595.
Chicago/Turabian StyleSa Wang; Lifu Zhang; Hongming Zhang; Xingxing Han; Linshan Zhang. 2020. "Spatial–Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A/B Data in the Dry Season from 1973–2019." Remote Sensing 12, no. 10: 1595.
The Everglades National Park (ENP) has one of the largest mangrove forests in the United States, yet due to lack of data and methods, there has been no multi‐decadal record of detailed changes in its mangrove forests, not to mention their response to periodic hurricanes. Here, based on remote sensing spectroscopy, multi‐sensor cross‐calibration, spectral normalization, and pixel unmixing, we develop a step‐wise method to map distributions and changes of the ENP mangrove forests and other major wetlands cover types (marshes, hardwood hammocks) in the last three decades. The time‐series of Landsat‐based results indicate statistically significant increasing trend from 1985 to 2017 in the total ENP mangrove coverage with a cumulated increase of 10.2 %, which has increased in the inner coastline area but decreased in the outer coastline area. The mangrove coverage also decreased considerably in certain hurricane years (1992, 2005, 2017), with the largest decrease of 644.9 km2 (46.5% of the mean mangrove area in normal years) occurring in 1993 after Hurricane ANDREW. Yet, these large mangrove die‐off areas gradually recovered to pre‐hurricane levels 3‐4 years after the passage of major hurricanes. About 3.2 km2 of mangrove forests were lost in the outer coastal area from 2005 to 2016, also due to hurricanes (especially Hurricane Katrina/Wilma in 2005). Results also indicate that while the mangrove forests were always damaged by hurricanes, the extent of the damage depended on wind speed, direction, and distance from maximum wind. The findings here could serve as baseline information for future restoration efforts of the ENP ecosystem, and the study also provides a method extendable to other coastal wetland regions.
Xingxing Han; Lian Feng; Chuanmin Hu; Philip Kramer. Hurricane‐Induced Changes in the Everglades National Park Mangrove Forest: Landsat Observations Between 1985 and 2017. Journal of Geophysical Research: Biogeosciences 2018, 123, 3470 -3488.
AMA StyleXingxing Han, Lian Feng, Chuanmin Hu, Philip Kramer. Hurricane‐Induced Changes in the Everglades National Park Mangrove Forest: Landsat Observations Between 1985 and 2017. Journal of Geophysical Research: Biogeosciences. 2018; 123 (11):3470-3488.
Chicago/Turabian StyleXingxing Han; Lian Feng; Chuanmin Hu; Philip Kramer. 2018. "Hurricane‐Induced Changes in the Everglades National Park Mangrove Forest: Landsat Observations Between 1985 and 2017." Journal of Geophysical Research: Biogeosciences 123, no. 11: 3470-3488.
Field and laboratory experiments are designed to measure Sargassum biomass per area (density), surface reflectance, nutrient contents, and pigment concentrations. An Alternative Floating Algae Index (AFAI)‐biomass density model is established to link the spectral reflectance to Sargassum biomass density, with a relative uncertainty of ~ 12%. Monthly mean integrated Sargassum biomass in the Caribbean Sea and Central West Atlantic reached at least 4.4 million tons in July 2015. The average % C, % N, and % P per dry‐weight are 27.16, 1.06, and 0.10, respectively. The mean chlorophyll‐a (Chl‐a) concentration is ~ 0.05% of the dry‐weight. With these parameters, the amounts of nutrients and pigments can be estimated directly from remotely sensed Sargassum biomass. During bloom seasons, Sargassum carbon can account for ~ 18% of the total particulate organic carbon in the upper water column. This study provides the first quantitative assessment of the overall Sargassum biomass, nutrients, and pigment abundance from remote‐sensing observations, thus helping to quantify their ecological roles and facilitate management decisions.
Mengqiu Wang; Chuanmin Hu; Jennifer Cannizzaro; David English; Xingxing Han; David Naar; Brian Lapointe; Rachel Brewton; Frank Hernandez. Remote Sensing of Sargassum Biomass, Nutrients, and Pigments. Geophysical Research Letters 2018, 45, 12,359 -12,367.
AMA StyleMengqiu Wang, Chuanmin Hu, Jennifer Cannizzaro, David English, Xingxing Han, David Naar, Brian Lapointe, Rachel Brewton, Frank Hernandez. Remote Sensing of Sargassum Biomass, Nutrients, and Pigments. Geophysical Research Letters. 2018; 45 (22):12,359-12,367.
Chicago/Turabian StyleMengqiu Wang; Chuanmin Hu; Jennifer Cannizzaro; David English; Xingxing Han; David Naar; Brian Lapointe; Rachel Brewton; Frank Hernandez. 2018. "Remote Sensing of Sargassum Biomass, Nutrients, and Pigments." Geophysical Research Letters 45, no. 22: 12,359-12,367.
Xingxing Han; Lian Feng; Chuanmin Hu; Xiaoling Chen. Wetland changes of China's largest freshwater lake and their linkage with the Three Gorges Dam. Remote Sensing of Environment 2018, 204, 799 -811.
AMA StyleXingxing Han, Lian Feng, Chuanmin Hu, Xiaoling Chen. Wetland changes of China's largest freshwater lake and their linkage with the Three Gorges Dam. Remote Sensing of Environment. 2018; 204 ():799-811.
Chicago/Turabian StyleXingxing Han; Lian Feng; Chuanmin Hu; Xiaoling Chen. 2018. "Wetland changes of China's largest freshwater lake and their linkage with the Three Gorges Dam." Remote Sensing of Environment 204, no. : 799-811.
Wetlands provide important ecosystem functions for water alteration and conservation of bio-diversity, yet they are vulnerable to both human activities and climate changes. Using four decades of Landsat and HJ-1A/1B satellites observations and recently developed classification algorithms, long-term wetland changes in Poyang Lake, the largest freshwater lake of China, have been investigated in this study. In dry seasons, while the transitions from mudflat to vegetation and vice versa were comparable before 2001, vegetation area increased by 620.8 km2 (16.6% of the lake area) between 2001 and 2013. In wet seasons, although no obvious land cover changes were observed between 1977 and 2003, ~ 30% of the Nanjishan Wetland National Nature Reserve (NWNNR) in the south lake changed from water to emerged plant during 2003 and 2014. The changing rate of the Normalized Difference Vegetation Index (NDVI) in dry seasons showed that the vegetation in the lake center regions flourished, while the growth of vegetation in the off-water areas was stressed. Rapid NDVI increase was also found in the NWNNR in the wet seasons. The relationships between the water levels and vegetation coverage also showed two regimes in both dry and wet seasons for the pre-Three Gorges Dam (TGD) period (before 2003) and post-TGD period (after 2003). Analyses of long-term hydrological and meteorological data clearly indicated that while local precipitation remained stable, the water level of Poyang Lake decreased significantly after the impoundment of the TGD, which is likely the main reason for the wetland expansion in recent years.
Lian Feng; Xingxing Han; Chuanmin Hu; Xiaoling Chen. Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam? Remote Sensing of Environment 2016, 176, 43 -55.
AMA StyleLian Feng, Xingxing Han, Chuanmin Hu, Xiaoling Chen. Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam? Remote Sensing of Environment. 2016; 176 ():43-55.
Chicago/Turabian StyleLian Feng; Xingxing Han; Chuanmin Hu; Xiaoling Chen. 2016. "Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam?" Remote Sensing of Environment 176, no. : 43-55.