Remote Sensing Reversion of Water Depths and Water Management for the Stopover Site of Siberian Cranes at Momoge, China

Jiang, Hongxing; Liu, Chunyue; Sun, Xinzhang; Lü, Jun; Zou, C. L.; Hou, Yunqiu; Lü, Xianguo

doi:10.1007/s13157-015-0626-6

Cited by 29 publications

(15 citation statements)

References 28 publications

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“…These opposite correlation signs for NDVI may also partially explain the low correction coefficient for NDVI and water level for the whole lake (Table 4). While a good index for monitoring green vegetation abundance in response to water level in vegetated wetlands such as marshes (Jiang et al 2015), NDVI proved not be appropriate for monitoring wetland hydrology in this saline lake with sparse vegetation.…”

Section: Temporal Behaviour Of Modis-derived Indices In Response To Imentioning

confidence: 95%

Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain

Vrieling

Skidmore

et al. 2015

Wetlands

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Monitoring spatio-temporal dynamics of hydrology in seasonally-flooded wetlands is important for water management and biodiversity conservation. Spectral data and derived indices from t he Moderate Resolution Imaging Spectroradiometer (MODIS) have been used for hydrological monitoring of large wetlands. However, comparable studies for small wetlands (<25 km 2) are lacking. Our aims are to examine whether MODIS-derived indices at 500-m spatial resolution can perform this task for small wetlands, and to compare the performance of various indices. First we evaluated if water levels are a good indicator for wetland inundation extent. A high correlation between water level and Landsatderived inundation extent was found (R 2 =0.957). Secondly, we compared 10 years of water level fluctuations with seven spectral indices at a 16-day interval. The Tasseled Cap brightness index (TCBI) had the highest correlation with water level for the complete time series including dry and wet years. Thirdly, we analyzed how these indices behave for areas with different inundation characteristics. Again TCBI showed a consistently accurate performance, which was independent of inundation frequency. We therefore conclude that TCBI is the best-suited index for monitoring of hydrological variability in small seasonally-flooded wetlands such as the Fuente de Piedra lake in southern Spain. We recommend testing this index further for other seasonally-flooded wetlands in semiarid areas.

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Section: Temporal Behaviour Of Modis-derived Indices In Response To Imentioning

confidence: 95%

Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain

Vrieling

Skidmore

et al. 2015

Wetlands

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“…To proceed further, we denote the mean and the covariance matrix of ω t as (19) where µ ω = 1 N ⊗ µ ω , ω = I N ⊗ ω , and µ ω = 0. Here, 1 N denotes an N dimensional vector with all the elements being 1, I N denotes an N ×N identical matrix, and ⊗ denotes the Kronecker product.…”

Section: B Computational Methodsmentioning

confidence: 99%

“…To tackle this challenge, various of advanced control strategies have been utilized to improve the performance of the USVs [10]. These strategies are designed for different control objectives [11]- [13], which, can generally be classified into three main categories: 1) Set point tracking [14]- [16]: this is the most common control target, which enables the USV's position and direction to reach the desired target without any time constraints; 2) Trajectory tracking [17]- [19]: USVs are driven to track reference signals varying with time and meeting pre-defined time and space constraints; 3) Path tracking [20], [21], [23]: the USVs are required to track a desired path that does not change over time. This paper will focus on the set point tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control for the Set Point Tracking of Unmanned Surface Vehicles

Yuan

Cai

et al. 2020

IEEE Access

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An unmanned surface vehicles (USV) set point tracking problem is investigated in this paper. The stochastic model predictive control (SMPC) scheme is utilized to design the controller in order to reject the environment disturbances and meet the physical constraints. The design problem is formulated as a chance-constrained stochastic optimization problem, which is non-convex. Thus, the problem is computationally prohibitive. For this, the convex conditional value at risk (CVaR) approximation is introduced to convert the chance constraints into deterministic convex constraints. The converted constraints are then further transformed into the second order cone (SOC) constraints. Therefore, the proposed method is computationally tractable and hence can be implemented online. A numerical example is provided to illustrate the effectiveness of the proposed method. INDEX TERMS Unmanned surface vehicles (USV), set point tracking, stochastic model predictive control, chance constraints, conditional value at risk (CVaR).

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“…Vegetation coverage measures the proportion of vertical projected areas of leaves, stems, and branches to the total area of the study site. Vegetation coverage well reflects the seasonal changes of vegetation growth, ecological environment, water/soil quality, and water conditions in wetlands and is a favorable indicator of seasonal changes in wetlands [18,19].…”

Section: Data Fusion Of the Study Areamentioning

confidence: 99%

Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Wang

Zhang

2018

J Image Video Proc.

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Given the seasonal dynamics of wetland ecosystem, limited available data, and technological method of wetland investigation, wetland evaluation cannot be accurately accessed. Although the remote sensing technology has been widely employed on wetland investigation and identification, changeable weather conditions especially cloud interference are the main barrier to acquire clear remote sensing image for wetland identification and information extraction. The combination and precision evaluation of remote sensing data with high temporal-spatial resolution ratio from moderate-resolution imaging spectroradiometer (MODIS) and Landsat were conducted using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM): a comprehensively temporal-spatial reflectance model was built; the high-resolution image in the time series and Modified Normalized Difference Water Index were obtained. The main data obstacles in wetland resources monitoring were invalid. The typical wetland areas in Liaoning province of China were evaluated using combination algorithm and Landsat (Thematic Mapper) images. The results show that the MODIS and Landsat data can be combined well with high correlations in different wave ranges. The maximum Normalized Difference Water Index (NDWI) is 0.9678, followed by green wave (0.9630), near-infrared wave (0.9345), and blue wave (0.9018).The wetland seasonal change of Panjin was examined using the data combination method. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and vegetation coverage index were extracted from combined images of Panjin from June 2016 to August 2016 and analyzed. Results showed that the NDVI was high in partial water area during the studied period indicating high chlorophyll contents.

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Remote Sensing Reversion of Water Depths and Water Management for the Stopover Site of Siberian Cranes at Momoge, China

Cited by 29 publications

References 28 publications

Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain

Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain

Stochastic Model Predictive Control for the Set Point Tracking of Unmanned Surface Vehicles

Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

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