Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions

2019 ◽  
Vol 33 (03) ◽  
pp. 518-529 ◽  
Author(s):  
Abhishek Kumar ◽  
Christopher Cooper ◽  
Caren M. Remillard ◽  
Shuvankar Ghosh ◽  
Austin Haney ◽  
...  
Keyword(s):  
South Carolina ◽  
Mississippi River ◽  
Water Resource Management ◽  
Large Scale ◽  
Light Attenuation ◽  
Hydrilla Verticillata ◽  
Army Corps ◽  
Landsat 8 ◽  
Management Actions ◽  
Physical Measurements

AbstractHydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. The negative impacts of hydrilla invasion have become a concern for water resource management authorities, power companies, and environmental scientists. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, in this study, we aimed to develop a tool for rapid, frequent, and large-scale monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 Operational Land Imager satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers lake east of the Mississippi River, located on the border of Georgia and South Carolina border. The predictive model for presence of hydrilla incorporated radiometric and physical measurements, including remote-sensing reflectance, Secchi disk depth (SDD), light-attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through the water column (PLW). The model-predicted ideal habitat for hydrilla featured high SDD, Zc, and PLW values, low values of Kd. Monthly analyses based on satellite images showed that hydrilla starts growing in April, reaches peak coverage around October, begins retreating in the following months, and disappears in February. Analysis of physical and meteorological factors (i.e., water temperature, surface runoff, net inflow, precipitation) revealed that these parameters are closely associated with hydrilla extent. Management agencies can use these results not only to plan removal efforts but also to evaluate and adapt their current mitigation efforts.

scholarly journals A Review of How Uncertainties in Management Decisions Are Addressed in Coastal Louisiana Restoration

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1528
Author(s):  
Angelina Freeman ◽  
James Pahl ◽  
Eric White ◽  
Summer Langlois ◽  
David Lindquist ◽  
...  
Keyword(s):  
Mississippi River ◽  
Flood Control ◽  
Large Scale ◽  
Oil And Gas ◽  
Project Planning ◽  
Lessons Learned ◽  
Management Decisions ◽  
Sea Levels ◽  
Management Actions ◽  
Coastal Louisiana

Louisiana has lost over 4800 km2 of coastal land since 1932, and a large-scale effort to restore coastal Louisiana is underway, guided by Louisiana’s Comprehensive Master Plan for a Sustainable Coast. This paper reviews science-based planning processes to address uncertainties in management decisions, and determine the most effective combination of restoration and flood risk reduction projects to reduce land loss, maintain and restore coastal environments, and sustain communities. The large-scale effort to restore coastal Louisiana is made more challenging by uncertainties in sediment in the Mississippi River, rising sea levels, subsidence, storms, oil and gas activities, flood-control levees, and navigation infrastructure. To inform decision making, CPRA uses structured approaches to incorporate science at all stages of restoration project planning and implementation to: (1) identify alternative management actions, (2) select the management action based on the best available science, and (3) assess performance of the implemented management decisions. Applied science and synthesis initiatives are critical for solving scientific and technical uncertainties in the successive stages of program and project management, from planning, implementation, operations, to monitoring and assessment. The processes developed and lessons learned from planning and implementing restoration in coastal Louisiana are relevant to other vulnerable coastal regions around the globe.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Mapping the Distribution of Water Resource Security in the Beijing-Tianjin-Hebei Region at the County Level under a Changing Context

2019 ◽  
Vol 11 (22) ◽  
pp. 6463 ◽  
Author(s):  
Li ◽  
Yin ◽  
Zhang ◽  
Croke ◽  
Guo ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Resource ◽  
Water Resource Management ◽  
Large Scale ◽  
Water Diversion ◽  
County Level ◽  
Populated Region ◽  
Water Resource Security ◽  
Resource Security

The Beijing-Tianjin-Hebei (Jingjinji) region is the most densely populated region in China and suffers from severe water resource shortage, with considerable water-related issues emerging under a changing context such as construction of water diversion projects (WDP), regional synergistic development, and climate change. To this end, this paper develops a framework to examine the water resource security for 200 counties in the Jingjinji region under these changes. Thus, county-level water resource security is assessed in terms of the long-term annual mean and selected typical years (i.e., dry, normal, and wet years), with and without the WDP, and under the current and projected future (i.e., regional synergistic development and climate change). The outcomes of such scenarios are assessed based on two water-crowding indicators, two use-to-availability indicators, and one composite indicator. Results indicate first that the water resources are distributed unevenly, relatively more abundant in the northeastern counties and extremely limited in the other counties. The water resources are very limited at the regional level, with the water availability per capita and per unit gross domestic product (GDP) being only 279/290 m3 and 46/18 m3 in the current and projected future scenarios, respectively, even when considering the WDP. Second, the population carrying capacity is currently the dominant influence, while economic development will be the controlling factor in the future for most middle and southern counties. This suggests that significant improvement in water-saving technologies, vigorous replacement of industries from high to low water consumption, as well as water from other supplies for large-scale applications are greatly needed. Third, the research identifies those counties most at risk to water scarcity and shows that most of them can be greatly relieved after supplementation by the planned WDP. Finally, more attention should be paid to the southern counties because their water resources are not only limited but also much more sensitive and vulnerable to climate change. This work should benefit water resource management and allocation decisions in the Jingjinji region, and the proposed assessment framework can be applied to other similar problems.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

Unified, high resolution water quality retrievals from Earth Observation satellites

2021 ◽  
Author(s):  
James Harding
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
State Of The Art ◽  
Earth Observation ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Atmospheric Conditions ◽  
Mixture Density ◽  
Wide Range

<p>Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. </p>

scholarly journals Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

2018 ◽  
Vol 10 (10) ◽  
pp. 1572 ◽  
Author(s):  
Chunping Qiu ◽  
Michael Schmitt ◽  
Lichao Mou ◽  
Pedram Ghamisi ◽  
Xiao Zhu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Landsat 8 ◽  
Climate Zone ◽  
Local Climate ◽  
Feature Importance ◽  
Local Climate Zone ◽  
Sentinel 2

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

scholarly journals Current Status of the Norman Site, 34WG2

2000 ◽  
Author(s):  
Louis E. Vogle
Keyword(s):  
Large Scale ◽  
Wave Action ◽  
Current Status ◽  
Highway Bridge ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Corps Of Engineers

As defined by Finkelstein in his description of excavations at the site, the Norman site currently is completely located within the waters of Fort Gibson Reservoir, a U.S. Army Corps of Engineers (COE) lake on the Grand (Neosho) River in northeastern Oklahoma. Due to a combination of archeological excavations at the site during the 1930s and 1940s, pothunting, large-scale earthmoving activities associated with the construction of a nearby highway bridge, and approximately 50 years of wave action and seasonal inundation by Fort Gibson Reservoir, portions of Mounds I-1 and I-2 are all that remain of the Norman site.

The Late Wisconsinan Savanna Terrace in Tributaries to the Upper Mississippi River

1983 ◽  
Vol 20 (2) ◽  
pp. 165-176 ◽  
Author(s):  
Mark A. Flock
Keyword(s):  
Mississippi River ◽  
Large Scale ◽  
Lake Superior ◽  
Mississippi Valley ◽  
Red Clay ◽  
Radiocarbon Dates ◽  
Jackson County ◽  
Late Wisconsinan ◽  
Different Sources ◽  
Lacustrine Deposit

The Savanna Terrace, composed of alternating red and gray clayey sediments of late Wisconsinan age, can be found in five states along the upper Mississippi valley from Pepin County, Wisconsin, to Jackson County, Illinois. The terrace is the highest glaciofluvial-lacustrine deposit without a loess cover in the upper Mississippi valley. Chemical, physical, and mineralogical data show that two different sources provided sediment. The red clay is believed to have come from Lake Superior sources, while the gray clay is believed to have come from sources farther west. Large-scale flood events from glacial Lakes Agassiz, Grantsburg, and Superior were probably the main contributors of the sediments. The red clay in the terrace is similar in composition to red glaciolacustrine sediment found in eastern and northern Wisconsin. It also is mineralogically similar to the Hinckley Sandstone and the Fond du Lac Formation, which occur under and around Lake Superior. Radiocarbon dates obtained from the lower Illinois valley indicate that the terrace sediments were deposited sometime between about 13,100 and 9500 yr ago. Soils developed on the terrace are variable in their physical, chemical, and mineralogical properties, which reflect the composition of the clayey sediments.

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