THE RELATIONSHIP BETWEEN SUMMER‐SEASON RAINFALL EVENTS AND LAKE‐SURFACE AREA<sup>1</sup>

Rundquist, Donald C.; Lawson, Merlin P.; Queen, Lloyd P.; Cerveny, Randall S.

doi:10.1111/j.1752-1688.1987.tb00828.x

Cited by 88 publications

(44 citation statements)

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“…The process begins by filtering the image using the four directional operators that are shown in Figure 6 and using Equation (6). Then, the LFE is equivalent to the maximum value of the four directions for each pixel.…”

Section: Narrow River Extractionmentioning

confidence: 99%

“…To eliminate the aforementioned difficulties that are associated with the Duda road operator, the LFE operator presented in Figure 6 considers only three pixels (the Duda road operator considers nine) at a time to detect the twisted portion of river (meanders). Equation (6) forces the values of pixels that are situated on stepwise edges to become 0: (6) Here, i is the index of the four directional operators, and a, b and c are the pixels for a single LFE operator ( Figure 6). …”

Section: Narrow River Extractionmentioning

confidence: 99%

“…(1) Spectral bands: These methods, which identify water bodies by applying thresholds to one or more spectral bands, are easy to implement, but often misclassify mountain shadows, urban areas or other background noise as water bodies [6]. (2) Classification: These methods apply supervised or unsupervised machine-learning algorithms to extract water bodies from multispectral imagery.…”

Section: Introductionmentioning

confidence: 99%

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An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

et al. 2014

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Abstract:The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and OPEN ACCESSRemote Sens. 2014, 6 5068 commission errors, edge position accuracy and completeness, were calculated to assess the method's performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

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Section: Narrow River Extractionmentioning

confidence: 99%

Section: Narrow River Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

et al. 2014

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“…To date, a number of water extraction techniques using optical imagery have been developed, which can be categorised into four basic types: (a) statistical pattern recognition techniques including supervised [4][5][6] and unsupervised classification methods [7]; (b) linear unmixing [8]; (c) single-band thresholding [9,10]; and (d) spectral indices [3,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Geng

Sun

et al. 2015

Water

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Extracting surface water distribution with satellite imagery has been an important subject in remote sensing. Spectral indices of water only use information from a limited number of bands, thus they may have poor performance from pixels contaminated by ice/snow, clouds, etc. The detection algorithms using information from all spectral bands, such as constrained energy minimization (CEM), could avoid this problem to some extent. However, these are mostly designed for hyperspectral imagery, and may fail when applied to multispectral data. It has been proved that adding linearly irrelevant data to original data could improve the performance of CEM. In this study, two kinds of linearly irrelevant data are added for water extraction: the spectral indices and the spectral similarity metric data. CEM is designed for targets with low-probability distribution in an image, but water bodies do not always satisfy this condition. We thereby impose a sensible coefficient for each pixel to form the weighted autocorrelation matrix. In this study, the weight is based on the orthogonal subspace projection, so this new method is named Orthogonal subspace projection Weighted CEM (OWCEM). The newly launched Landsat 8 images over two lakes, the Hala Lake in China with ice/snow distributed in the north, and the Huron Lake in OPEN ACCESSWater 2015, 7 795North America, a lake with a very large surface area, are selected to test the accuracy and robustness of our algorithm. The Kappa coefficient and the receiver operating characteristic (ROC) curve are calculated as an accuracy evaluation standard. For both lakes, our method can greatly suppress the background (including ice/snow and clouds) and extract the complete water surface with a high accuracy (Kappa coefficient > 0.96).

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“…Although remote sensing methods have been extensively used to monitor changes in the surface water extent of potholes and other terminal lakes (e.g., [80][81][82]), few of the results have been used to support specific environmental policies, regulatory actions, or for setting/measuring conservation program goals. The lack of policy linkages to the ongoing research is particularly unfortunate because the remote sensing techniques in the PPR are very reproducible (with great potential for use in other depressional wetlands) and require relatively simple analytical techniques, such as traditional classification and density slice approaches (e.g., [83][84][85][86]).…”

Section: (A) (B) (C)mentioning

confidence: 99%

Use of Remote Sensing to Support Forest and Wetlands Policies in the USA

Mayer

Lopez

2011

Remote Sensing

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Abstract:The use of remote sensing for environmental policy development is now quite common and well-documented, as images from remote sensing platforms are often used to focus attention on emerging environmental issues and spur debate on potential policy solutions. However, its use in policy implementation and evaluation has not been examined in much detail. Here we examine the use of remote sensing to support the implementation and enforcement of policies regarding the conservation of forests and wetlands in the USA. Specifically, we focus on the "Roadless Rule" and "Travel Management Rules" as enforced by the US Department of Agriculture Forest Service on national forests, and the "No Net Loss" policy and Clean Water Act for wetlands on public and private lands, as enforced by the US Environmental Protection Agency and the US Army Corps of Engineers. We discuss several national and regional examples of how remote sensing for forest and wetland conservation has been effectively integrated with policy decisions, along with barriers to further integration. Some of these barriers are financial and technical (such as the lack of data at scales appropriate to policy enforcement), while others are political.

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THE RELATIONSHIP BETWEEN SUMMER‐SEASON RAINFALL EVENTS AND LAKE‐SURFACE AREA¹

Cited by 88 publications

References 0 publications

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Use of Remote Sensing to Support Forest and Wetlands Policies in the USA

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THE RELATIONSHIP BETWEEN SUMMER‐SEASON RAINFALL EVENTS AND LAKE‐SURFACE AREA1

Cited by 88 publications

References 0 publications

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Use of Remote Sensing to Support Forest and Wetlands Policies in the USA

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THE RELATIONSHIP BETWEEN SUMMER‐SEASON RAINFALL EVENTS AND LAKE‐SURFACE AREA¹