scholarly journals Dynamic Monitoring and Analysis of Ecological Quality of Pingtan Comprehensive Experimental Zone, a New Type of Sea Island City, Based on RSEI

2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Xiaole Wen ◽  
Yanli Ming ◽  
Yonggang Gao ◽  
Xinyu Hu
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Ecological Status ◽  
Dynamic Monitoring ◽  
Ecological Quality ◽  
Ecological Planning ◽  
Large Area ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Ecological Index

Islands face increasingly prominent environmental problems with rapid urbanization. Hence, timely and objective monitoring and evaluation of island ecology is of great significance. This study took the Pingtan Comprehensive Experimental Zone (PZ) in the east sea of Fujian Province of China as the research object. Based on remote sensing technology, four Landsat images from 2007 to 2017 and the remote sensing ecological index (RSEI) were used to explore the ecological status and space–time change. The results showed that from 2007 to 2011, the average RSEI decreased from 0.519 to 0.506, indicating that the ecological quality generally showed a slight downward trend, mainly due to large-scale development brought by the construction; by 2014, although the ecology of the original area improved, the overall ecology was still declining with 0.502 mean RSEI mainly because of large-scale reclamation projects; by 2017, the average RSEI rebounded to 0.523, which was attributed to the fact that ecological construction and protection were emphasized in the construction of PZ, especially in reclamation areas. In conclusion, the increase of large area bare soil will lead to the decline of regional ecology, but the implementation of scientific ecological planning is conducive to ecological restoration and construction.

Spectral Characteristics of Cr and Cu in Agricultural Soil at Beijing Plain Based on SWIR Spectroscopy and PLSR

2013 ◽  
Vol 765-767 ◽  
pp. 3066-3072 ◽  
Author(s):  
Shu Min Li ◽  
Hong Li ◽  
Dan Feng Sun ◽  
Lian Di Zhou
Keyword(s):  
Heavy Metals ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Large Scale ◽  
Spectral Characteristics ◽  
Dynamic Monitoring ◽  
Optimum Number ◽  
Least Squares Regression ◽  
Large Area ◽  
Soil Heavy Metals

Heavy metals pollution in agricultural soils has been an important problem to human health, mapping large-scale spatial distribution of soil heavy metals is urgently needed. Instead of traditional methods, time-consuming and destructive, soil properties predicted by remote sensing technology shows a lot of advantages, which makes large area of real-time dynamic monitoring as possible. However, before achieving prediction using spectra data, the first thing to do is that finding the spectral characteristics of soil heavy metals. In this paper, taking Cr and Cu for example, the correlations between soil heavy metals content and laboratory-measured reflectance is studied using partial least squares regression (PLSR), which is an adaptive method to examine linear between spectrum and concentration. First of all, using the raw spectra, remove outliers of heavy metals concentration by PLSR modeling. Next, though comparing RMSEC and RMSEV against PLSR components, and cumulative explanatory of spectral components to metal content using different pre-precessing methods, find the right pre-pcocessing is CR and optimum number of components to Cr and Cu are 3 and 2 respectively. Simultaneously, with the meaning of PLSR models regression coefficients, we analysis the spectral characteristics of Cr and Cu, although can not to realize the prediction only take use of these spectra, which is still essential to achieve simulating spatial distribution of soil heavy metal by remote sensing.

scholarly journals Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI

2020 ◽  
Vol 12 (18) ◽  
pp. 7716
Author(s):  
Pengwen Gao ◽  
Alimujiang Kasimu ◽  
Yongyu Zhao ◽  
Bing Lin ◽  
Jinpeng Chai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Explanatory Power ◽  
Ecological Status ◽  
Quality Level ◽  
Ecological Quality ◽  
P Values ◽  
Ecological Index ◽  
Poor Grade ◽  
Window Method

Given the restrictions on special geographic locations in development processes, the measurement and analysis of the ecological quality of the Hami Oasis are of great significance for the protection of this fragile oasis. In this study, the ecological quality of the Hami Oasis was monitored by constructing a remote sensing ecological index (RSEI) for arid areas. Using the standard deviation ellipse and moving window method, the ecological status and space–time changes were explored for both their external and internal factors in the Hami Oasis. Finally, a geo-detector was employed to determine the driving factors of the ecological quality of the Hami Oasis. The results revealed that: (1) In the remote sensing ecological index constructed in the Hami Oasis, the main influencing factors were dryness and wetness. The average value of the ecological quality of the oasis was less than 0.5, and the ecological quality level was relatively poor. Among the five grades of ecological quality in the Hami Oasis, the poor grade and the good grade showed the largest changes, decreasing by 200 and increasing by 300, respectively, which were mainly concentrated in the periphery of the oasis. (2) The improved ecological quality of the Hami Oasis was mainly manifested in the expansion of the artificial oasis, while the deteriorated area was manifested as an increase in the built-up area. Moreover, the ecological quality of the Hami Oasis presented a ringlike nesting distribution pattern from the internal built-up area to the artificial oasis periphery. (3) The external expansion direction of the ecological quality of the Hami Oasis featured southeast–northwest expansion, which was consistent with the direction of the rivers and traffic roads. The transformation between different ecological qualities in the oasis and the expansion of the built-up area were the reasons for the fragmentation of the Hami Oasis’ landscape. (4) Compared to a single factor, the dual-factor for the ecological quality of the Hami Oasis had stronger explanatory power. Moreover, changes in land use types caused changes in the ecological quality of the Hami Oasis. During the study period, we found that human activities had a more significant impact than natural factors on the development of the Hami Oasis. (5) The Moran’s I Index increased from 0.835268 in 2000 to 0.923976 in 2018, and the p values in the study area all reached a 0.05 significant level. At the same time, the areas with p values above the 0.01 and 0.001 significant levels have also increased significantly in the past 18 years.

scholarly journals Land Surface Temperature Mapping using Geoinformation Techniques

2018 ◽  
Vol 17 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Chukwuka Friday Agbor ◽  
Esther Oluwafunmilayo Makinde
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Data ◽  
Software Systems ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Temperature Mapping

General environmental management, which involves monitoring and modeling, requires the information of the Land surface temperature (LST) status of area concerned. Land surface temperature has gained relevance recognition over the years and there is need to develop approaches that can determine LST using satellite images. This study was conducted in Akure which has experienced rapid urbanization in recent time. The study utilized Landsat data of 1984, 1990, 2000, 2003, 2014 and 2016. The temperature data were derived from Landsat images using remote sensing algorithms for assessing LST from thermal infrared (TIR) data (bands 6 and 10). These data were processed and analyzed using tools in Idrisi and ArcGIS software systems. Satellite-derived land surface temperatures were validated with in-situ temperature data. The results revealed parabolic increase in temperature over the years and the changing pattern was investigated by adopting existing ecological indexes.. The validation operation revealed average bias value of between remote sensing- and ground-based data. This implies that remote sensing technique is reliable and therefore could be employed for large scale temperature mapping. The results could be used in mitigating urban heat island effectssuch as heat-related stress and ill-timed human deaths.

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

scholarly journals Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

2021 ◽  
Vol 10 (7) ◽  
pp. 475
Author(s):  
Ting Zhang ◽  
Ruiqing Yang ◽  
Yibo Yang ◽  
Long Li ◽  
Longqian Chen
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Urban Expansion ◽  
Assessment Method ◽  
Landsat Images ◽  
Ecological Index ◽  
Environment Quality ◽  
Quality Assessment Method ◽  
The Impact

The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

scholarly journals Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results

2021 ◽  
Vol 13 (19) ◽  
pp. 3970
Author(s):  
Huan Zhao ◽  
Junsheng Li ◽  
Xiang Yan ◽  
Shengzhong Fang ◽  
Yichen Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Cyanobacterial Bloom ◽  
Remote Sensing Data ◽  
Cyanobacterial Blooms ◽  
Dynamic Monitoring ◽  
Dianchi Lake ◽  
Sensing Technology ◽  
Multispectral Remote Sensing ◽  
Early Warnings

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes.

scholarly journals Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3472
Author(s):  
Yuming Wei ◽  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Roberto Tomás ◽  
Zhuo Jiang
Keyword(s):  
Remote Sensing ◽  
Loess Plateau ◽  
High Precision ◽  
Large Scale ◽  
Surface Displacement ◽  
Optical Remote Sensing ◽  
Consolidation Process ◽  
Rapid Urbanization ◽  
Sar Imagery ◽  
The City

Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than −40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.

scholarly journals A color balancing method for wide range Remote Sensing imagery based on Regionalization

2015 ◽  
Vol XL-7/W4 ◽  
pp. 103-108 ◽  
Author(s):  
J. Liu ◽  
H. T. Li ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Difficult Problem ◽  
Geographic Information ◽  
Dynamic Monitoring ◽  
Land Resource ◽  
Climatic Data ◽  
Large Area ◽  
Remote Sensing Imagery ◽  
Wide Range ◽  
Algorithmic Framework

Quick mosaicking of wide range remote sensing imagery is an important foundation for land resource survey and dynamic monitoring of environment and nature disasters. It is also technically important for basis imagery of geographic information acquiring and geographic information product updating. This paper mainly focuses on one key technique of mosaicking, color balancing for wide range Remote Sensing imagery. Due to huge amount of data, large covering rage, great variety of climate and geographical condition, color balancing for wide range remote sensing imagery is a difficult problem. In this paper we use Ecogeographic regionalization to divide the large area into several regions based on terrains and climatic data, construct the algorithmic framework of a color balancing method according to the regionalization result, which conduct from region edge to center to fit wide range imagery mosaicking. The experimental results with wide range HJ-1 dataset show that our method can significantly improve the wide range of remote sensing imagery color balancing effects: making images well-proportioned mosaicking and better in keeping images' original information. In summary, this color balancing method based on regionalization could be a good solution for nationwide remote sensing image color balancing and mosaicking.

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