scholarly journals Unsupervised classification of saturated areas using a time series of remotely sensed images

2007 ◽  
Vol 4 (3) ◽  
pp. 1663-1696 ◽  
Author(s):  
D. A. DeAlwis ◽  
Z. M. Easton ◽  
H. E. Dahlke ◽  
W. D. Philpot ◽  
T. S. Steenhuis
Keyword(s):  
Water Content ◽  
Management Practices ◽  
Spatial Information ◽  
New York State ◽  
Soil Surface ◽  
Resource Planning ◽  
Remotely Sensed ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Remotely Sensed Images

Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape.

scholarly journals Unsupervised classification of saturated areas using a time series of remotely sensed images

2007 ◽  
Vol 11 (5) ◽  
pp. 1609-1620 ◽  
Author(s):  
D. A. de Alwis ◽  
Z. M. Easton ◽  
H. E. Dahlke ◽  
W. D. Philpot ◽  
T. S. Steenhuis
Keyword(s):  
Surface Water ◽  
Water Content ◽  
Management Practices ◽  
Spatial Information ◽  
New York State ◽  
Resource Planning ◽  
Remotely Sensed ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Remotely Sensed Images

Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or siting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. A technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs. This study describes a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution Landsat 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas susceptible to saturation. We found that within a single land cover, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of the vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas (accuracies from 49 to 79%). This methodology shows that remote sensing can be used to capture temporal variations in vegetation phenology as well as spatial/temporal variation in surface water content, and appears promising for delineating saturated areas in the landscape.

scholarly journals FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images

2022 ◽  
Vol 14 (1) ◽  
pp. 215
Author(s):  
Xuerui Niu ◽  
Qiaolin Zeng ◽  
Xiaobo Luo ◽  
Liangfu Chen
Keyword(s):  
Spatial Information ◽  
Satellite Image ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Feature Representation ◽  
Position Information ◽  
Feature Information ◽  
Remotely Sensed Images ◽  
Public Datasets ◽  
Fine Resolution

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.

scholarly journals SEARCHING REMOTELY SENSED IMAGES FOR MEANINGFUL NESTED GESTALTEN

2016 ◽  
Vol XLI-B3 ◽  
pp. 899-903
Author(s):  
E. Michaelsen ◽  
D. Muench ◽  
M. Arens
Keyword(s):  
High Probability ◽  
Ground Truth ◽  
Mathematical Structure ◽  
Remotely Sensed ◽  
Input Image ◽  
Small Subset ◽  
Automatic Extraction ◽  
Remotely Sensed Data ◽  
Virtual Globe ◽  
Remotely Sensed Images

Even non-expert human observers sometimes still outperform automatic extraction of man-made objects from remotely sensed data. We conjecture that some of this remarkable capability can be explained by Gestalt mechanisms. Gestalt algebra gives a mathematical structure capturing such part-aggregate relations and the laws to form an aggregate called Gestalt. Primitive Gestalten are obtained from an input image and the space of all possible Gestalt algebra terms is searched for well-assessed instances. This can be a very challenging combinatorial effort. The contribution at hand gives some tools and structures unfolding a finite and comparably small subset of the possible combinations. Yet, the intended Gestalten still are contained and found with high probability and moderate efforts. Experiments are made with images obtained from a virtual globe system, and use the SIFT method for extraction of the primitive Gestalten. Comparison is made with manually extracted ground-truth Gestalten salient to human observers.

Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends

2008 ◽  
Vol 32 (5) ◽  
pp. 503-528 ◽  
Author(s):  
Steve N. Gillanders ◽  
Nicholas C. Coops ◽  
Michael A. Wulder ◽  
Sarah E. Gergel ◽  
Trisalyn Nelson
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Landscape Pattern ◽  
Pattern Analysis ◽  
Spatial Information ◽  
Remotely Sensed ◽  
Spatial Pattern Analysis ◽  
Remotely Sensed Data ◽  
Landscape Pattern Analysis ◽  
Landscape Pattern Indices

Science and reporting information needs for monitoring dynamics in land cover over time have prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Change detection procedures based upon spectral values are common; however, landscape pattern analysis approaches which utilize spatial information inherent within imagery present opportunities for the generation of unique and ecologically important information. While the use of two images may provide the means to identify change, the use of more than two images for long-term monitoring affords the ability to identify a greater range of processes of landscape change, including rates and dynamics. The main objective of this review is to investigate and summarize the methods and applications of land cover spatial pattern analysis using three or more image dates. The potential and the limitations of landscape pattern indices are identified and discussed to inform application recommendations. The second objective of this review is to make recommendations, including appropriate landscape pattern indices, for the application of landscape pattern analysis of a long time series of remotely sensed data to a case study involving the mountain pine beetle in British Columbia, Canada. The review concludes with recommendations for future research.

scholarly journals INTEGRATION OF REMOTELY SENSED DATA INTO GEOSPATIAL REFERENCE INFORMATION DATABASES. UN-GGIM NATIONAL APPROACH

2016 ◽  
Vol XLI-B4 ◽  
pp. 721-725
Author(s):  
A. Arozarena ◽  
G. Villa ◽  
N. Valcárcel ◽  
B. Pérez
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Remotely Sensed ◽  
Geographic Information ◽  
Earth Observation ◽  
Institutional Arrangements ◽  
Observation System ◽  
Remotely Sensed Data ◽  
Geospatial Information ◽  
Reference Information

Remote sensing satellites, together with aerial and terrestrial platforms (mobile and fixed), produce nowadays huge amounts of data coming from a wide variety of sensors. These datasets serve as main data sources for the extraction of Geospatial Reference Information (GRI), constituting the “skeleton” of any Spatial Data Infrastructure (SDI). <br><br> Since very different situations can be found around the world in terms of geographic information production and management, the generation of global GRI datasets seems extremely challenging. Remotely sensed data, due to its wide availability nowadays, is able to provide fundamental sources for any production or management system present in different countries. After several automatic and semiautomatic processes including ancillary data, the extracted geospatial information is ready to become part of the GRI databases. <br><br> In order to optimize these data flows for the production of high quality geospatial information and to promote its use to address global challenges several initiatives at national, continental and global levels have been put in place, such as European INSPIRE initiative and Copernicus Programme, and global initiatives such as the Group on Earth Observation/Global Earth Observation System of Systems (GEO/GEOSS) and United Nations Global Geospatial Information Management (UN-GGIM). These workflows are established mainly by public organizations, with the adequate institutional arrangements at national, regional or global levels. Other initiatives, such as Volunteered Geographic Information (VGI), on the other hand may contribute to maintain the GRI databases updated. <br><br> Remotely sensed data hence becomes one of the main pillars underpinning the establishment of a global SDI, as those datasets will be used by public agencies or institutions as well as by volunteers to extract the required spatial information that in turn will feed the GRI databases. <br><br> This paper intends to provide an example of how institutional arrangements and cooperative production systems can be set up at any territorial level in order to exploit remotely sensed data in the most intensive manner, taking advantage of all its potential.

Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia

2003 ◽  
Vol 12 (2) ◽  
pp. 185 ◽  
Author(s):  
Kate Brandis ◽  
Carol Jacobson
Keyword(s):  
Fire History ◽  
Spatial Information ◽  
New South ◽  
New South Wales ◽  
Remotely Sensed ◽  
Landsat Tm ◽  
Remotely Sensed Data ◽  
Fuel Loads ◽  
South Wales ◽  
The Impact

Fuel loads in forest areas are dependent on vegetation type and the time since the last fire. This paper reports a study on the feasibility of using remotely sensed data to estimate vegetative fuel loads. It describes two methods for estimating fuel loads using Landsat TM data based on equations describing litter accumulation and decomposition. The first method uses classification techniques to predict vegetation types coupled with fire history data to derive current fuel loads. The second method applies a canopy turnover rate to estimate litterfall and subsequently accumulated litter from biomass, thus utilising the dominant influence of canopy on remotely sensed data. Both methods are compared with data collected from Popran National Park in coastal New South Wales. The amounts of litter calculated with the biomass method were similar to field results, but the classification method was found to overestimate fuel loads. A sensitivity analysis investigated the impact of varying the vegetation constants and rates used in the fuel estimates to simulate uncertainty or error in their values. The biomass method was less subject to uncertainties and has potential for estimating fuel quantities to provide useful spatial information for fire managers.

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