Study on remote sensing retrieval of urban thermal environment and the temporal-spatial distribution in Beijing-capital zone

Urban surface temperature is higher than surrounding regions, which is associated with the differences between the urban surface and it surroundings and arising serious of environmental problem. In the paper, we retrieved land surface temperature of Beijing-capital zone by spit windows algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) image at I km resolution, which kept good similarity with LST product of MODIS, then we analyzed the temporal-spatial change dynamic using synthesis eight days LST product. It was found that the daytime UHI demonstrates distinctive seasonal variation, with the minimum 21 degrees C in all four seasons. The UHI effect of night is more serious than that of day time in Jan. and Jul.; while vice verse in Apr. and Oct. The UHI intensity rank is Apr., Jan., Oct., Jul. by decreasing in day time respectively, and Jan., Apr., Jul., Oct. in night time. The spatial autocorrelation analysis indicates that the distribution of thermal environment is spatial clustering of similar LST value. Distribution of UHI effect and the correlationship between landuse/landcover and UHI effect are analyzed. The conclusion shows that the urban heat island mainly results from the difference of the surface thermal characteristics between urban and rural area

Long-term monitoring and phenological analysis of submerged aquatic vegetation in a shallow lake using time-series imagery

Submerged aquatic vegetation (SAV) is a key functional group for the restoration of wetlands and aquatic ecosystems. It plays a vital role in the stability of ecosystem structure and function in shallow lakes and reservoirs. With Baiyangdian Lake as the research area, Landsat TM/ETM+/OLI and Sentinel-2 imagery datasets were used to explore the phenological characteristics of SAV over different time scales and construct a phenological map of the SAV groups. The long-term spatial distribution characteristics and trends of SAV from 1986 to 2021 were analyzed. The results show the following: (1) the harmonic analysis of time series (HANTS) eliminated abnormal observations and noise. Normalized difference vegetation index (NDVI) time series curves generated after HANTS accurately reflected the phenological characteristics of SAV. The early spring submerged aquatic vegetation (SAV1), such as Potamogeton crispus, entered the germination period in late February at the earliest (DOY = 53). The mature period ran from April to May, after which SAV1 began to decline gradually. The length of the growing season (LOS) was about 110 days. SAV2, which was represented by Ceratophyllum demersum, Myriophyllum verticillatum, and Potamogeton pectinatus, germinated in early April (DOY = 99). The mature period ran from July to September. The length of the growing season was 210 days. (2) The decision tree constructed based on the NDVI and modified normalized difference water index (MNDWI) identified SAV with an accuracy of 89.7%. (3) The distribution range of the SAV changed dramatically during 1986–2021. According to the area change, it can be divided into a shrinkage period, expansion period, degeneration period, and initial period of recovery. The succession trend of SAV provide a theoretical scientific basis and technical support for the ecological restoration and management of shallow lake ecosystems

Remote Sensing Monitoring of the Bottom Topography in a Shallow Reservoir and the Spatiotemporal Changes of Submerged Aquatic Vegetation Under Water Depth Fluctuations

Monitoring the growth and distribution of submerged aquatic vegetation (SAV) is crucial to the protection and restoration of the ecosystem of inland reservoirs. Considering the high sensitivity of SAV to water depth fluctuations in Guanting Reservoir, China, in this study, we realized the reconstruction of bottom topography by combining changing water level with a long time series remote sensing technology and explored the spatiotemporal succession law of SAV by analyzing the effect of water depth on the spatiotemporal distribution of SAV. Results of water depth spatial distribution in Guanting Reservoir were obtained by using water and land boundary lines to construct underwater terrain contours. The accuracy of estimated water depth data from remote sensing images was verified with measured water depth data, and the average relative error of water depth estimation results was about 0.25 m. The experimental results show that (1) the SWIR bands of Landsat images could avoid the interference of aquatic vegetation and realize the separation of land and water; and (2) after separating water area from land, an SWIR1_NIR index was used to effectively map SAV distribution in the reservoir. The results also indicate that the distribution of SAV in the reservoir is suitable for the water depth range of 0−2 m. Water depth fluctuations cause changes in the spatial distribution of suitable water depth. It is the main reason for the change of SAV distribution area in the reservoir during the past 20 years

Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments

Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other

Spatial and Temporal Trend of Water Resources in Beijing, China during 1999-2012 and Its Impact Analysis

Aims: The objective of this research is to understand and analyze the trend of water resources and its effects on land subsidence, vegetation cover change, and water supply reservoir drawdown in Beijing, China.Study Design: This research combined both field monitoring data and remote sensing data to study the water resource change and its impacts in the City of Beijing, China.Place and Duration of Study: This study water resource record data during 1999-2012 and the Landsat TM or ETM+ data in the same period to analyze the changes of water resources in the City of Beijing. The water level and water surface change of the Guanting Reservoir as a major urban water supply was analyzed since 1979 in confirmation of the trend of water resource change.Methodology: This research applied remote sensing data analysis, GIS spatial and spatial statistical analysis, and the conventional field monitoring data of water resources to understand and visualize the trend of water resource change and its environmental impacts in Beijing, China.Results: This research shows that both surface water and ground water resources are declining owing to the decrease of precipitation in Beijing. In the meantime, the proportion of agricultural and industrial water consumptions was gradually reduced and that of urban and domestic water consumptions continuously increased. Land subsidence spatially coincides with groundwater level decrease, and the maximum quantity could reach five meters. Vegetation cover and NDVI index showed high correlation with precipitation in mountainous region, but does not reflect the natural water supply in plain regions in Beijing. The surface water area in the Guanting Reservoir drastically reduced since 1979.Conclusion: The trend of water resource changes indicate that the water supply shortage in Beijing area was intensified. The more effective planning of economic development and urban growth in Beijing according its water resources is needed

Above-Bottom Biomass Retrieval of Aquatic Plants with Regression Models and SfM Data acquired by a UAV Platform – A Case Study in Wild Duck Lake Wetland, Beijing, China

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated

Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images

Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology–pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology–pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user’s accuracy and producer’s accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems

Dynamic Simulation of Vegetation Abundance in a Reservoir Riparian Zone using a Sub-Pixel Markov Model

Vegetation abundance is a significant indicator for measuring the coverage of plant community. It is also a fundamental data for the evaluation of a reservoir riparian zone eco-environment. In this study, a sub-pixel Markov model was introduced and applied to simulate dynamics of vegetation abundance in the Guanting Reservoir Riparian zone based on seven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager data acquired between 2001 and 2013. Our study extended Markov model\u27s application from a traditional regional scale to a sub-pixel scale. Firstly, Linear Spectral Mixture Analysis (LSMA) was used to obtain fractional images with a five-endmember model consisting of terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil. Then, a sub-pixel transitive probability matrix was calculated. Based on the matrix, we simulated statuses of vegetation abundance in 2010 and 2013, which were compared with the results created by LSMA. Validations showed that there were only slight differences between the LSMA derived results and the simulated terrestrial plants fractional images for both 2010 and 2013, while obvious differences existed for aquatic plants fractional images, which might be attributed to a dramatically diversity of water level and water discharge between 2001 and 2013. Moreover, the sub-pixel Markov model could lead to an RMSE (Root Mean Square Error) of 0.105 and an R2 of 0.808 for terrestrial plants, and an RMSE of 0.044 and an R2 of 0.784 for aquatic plants in 2010. For the simulated results with the 2013 image, an RMSE of 0.126 and an R2 of 0.768 could be achieved for terrestrial plants, and an RMSE of 0.086 and an R2 of 0.779 could be yielded for aquatic plants. These results suggested that the sub-pixel Markov model could yield a reasonable result in a short period. Additionally, an analysis of dynamics of vegetation abundance from 2001 to 2020 indicated that there existed an increasing trend for the average fractional value of terrestrial plants and a decreasing trend for aquatic plants

Estimating Wetland Vegetation Abundance from Landsat-8 Operational Land Imager Imagery: A Comparison between Linear Spectral Mixture Analysis and Multinomial Logit Modeling Methods

Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels