Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km² from 1987 to 2002, and it decreased to 2020.78 km² from 2005 to 2015, with a loss of 241.39 km² (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km², 44.40 km², 45.39 km², and 31.18 km², respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km², 322.71 km², 460.35 km², 400.79 km², 535.51 km², and 635.42 km² under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 10³ km², and seasonal and permanent water areas are 20.51 × 10³ km² and 28.01 × 10³ km², respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km², and the permanent water area increased by 3565 km² in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area.

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km² in 1989 and a minimum of 44 km² in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

The catastrophic 8.0 Richter magnitude earthquake that occurred on 12 May 2008 in Wenchuan, China caused extensive damage to vegetation due to widespread landslides and debris flows. In the past five years, the Chinese government has implemented a series of measures to restore the vegetation in the severely afflicted area. How is the vegetation recovering? It is necessary and important to evaluate the vegetation recovery effect in earthquake-stricken areas. Based on MODIS NDVI data from 2005 to 2013, the vegetation damage area was extracted by the quantified threshold detection method. The vegetation recovery rate after five years following the earthquake was evaluated with respect to counties, altitude, fault zones, earthquake intensity, soil texture and vegetation types, and assessed over time. We have proposed a new method to obtain the threshold with vegetation damage quantitatively, and have concluded that: (1) The threshold with vegetation damage was 13.47%, and 62.09% of the field points were located in the extracted damaged area; (2) The total vegetation damage area was 475,688 ha, which accounts for 14.34% of the study area and was primarily distributed in the central fault zone, the southwest mountainous areas and along rivers in the Midwest region of the study area; (3) Vegetation recovery in the damaged area was better in the northeast regions of the study area, and in the western portion of the Wenchuan-Maoxian fracture; vegetation recovery was better with increasing altitude; there is no obvious relationship between clay content in the topsoil and vegetation recovery; (4) Meadows recovered best and the worst recovery was in mixed coniferous broad-leaved forest; (5) 81,338 ha of vegetation in the damage area is currently undergoing degradation and the main vegetation types in the degradation area are coniferous forest (31.39%) and scrub (34.17%); (6) From 2009 to 2013, 41% has been restored to the level before the earthquake, 9% has not returned but 50% will continue to recover. The Chinese government usually requires five years as a period for post-disaster reconstruction. This paper could be regarded as a guidance for Chinese government departments, whereby additional investment is encouraged for vegetation recovery.