Geostatistics and Remote Sensing as Predictive Tools of Tick Distribution: a Cokriging System to Estimate Ixodes scapularis (Acari: Ixodidae) Habitat Suitability in the United States and Canada from Advanced Very High Resolution Radiometer Satellite Imagery

1998 ◽  
Vol 35 (6) ◽  
pp. 989-995 ◽  
Author(s):  
Agustin Estrada-Peña
Keyword(s):  
United States ◽  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Habitat Suitability ◽  
Ixodes Scapularis ◽  
The United States ◽  
Predictive Tools ◽  
Tick Distribution ◽  
Very High

scholarly journals Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1912
Author(s):  
Zhili Zhang ◽  
Meng Lu ◽  
Shunping Ji ◽  
Huafen Yu ◽  
Chenhui Nie
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Water Body ◽  
Feature Fusion ◽  
Feature Representation ◽  
Water Bodies ◽  
Feature Maps ◽  
Very High

Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.

scholarly journals Initial Observations of Psychological and Behavioral Effects of COVID-19 in the United States, Using Google Trends Data

2020 ◽  
Author(s):  
Daniel S Goldman
Keyword(s):  
United States ◽  
High Resolution ◽  
The United States ◽  
Behavioral Effects ◽  
Google Trends ◽  
Psychological States ◽  
Search Terms ◽  
Rapid Changes ◽  
Trend Data ◽  
Very High

Google Trends offers researchers a glimpse inside the minds of a large group of people, all at once, and offers a very high resolution as well. As a result, trend data may be useful for identifying rapid changes in day to day psychological states and behaviors associated with the COVID-19 outbreak. This paper analyzes stress related search terms as well as food related search terms, and identifies changes in patterns for each that appear to be associated with the outbreak.

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

Identification of Potential Freight Parking Locations in Urban Areas

2021 ◽  
pp. 036119812110130
Author(s):  
Glenn Vorhes ◽  
Ernest Perry ◽  
Soyoung Ahn
Keyword(s):  
United States ◽  
Information System ◽  
Satellite Imagery ◽  
Urban Areas ◽  
The United States ◽  
Truck Drivers ◽  
Short List ◽  
Automated Processes ◽  
Parking Facilities ◽  
Urban Sites

Truck parking is a crucial element of the United States’ transportation system as it provides truckers with safe places to rest and stage for deliveries. Demand for truck parking spaces exceeds supply and shortages are especially common in and around urban areas. Freight operations are negatively affected as truck drivers are unable to park in logistically ideal locations. Drivers may resort to unsafe practices such as parking on ramps or in abandoned lots. This report seeks to examine the potential parking availability of vacant urban parcels by establishing a methodology to identify parcels and examining whether the identified parcels are suitable for truck parking. Previous research has demonstrated that affordable, accessible parcels are available to accommodate truck parking. When used in conjunction with other policies, adaptation of urban sites could help reduce the severity of truck parking shortages. Geographic information system parcel and roadway data were obtained for one urban area in each of the 10 Mid America Association of Transportation Officials region states. Area and proximity filters were applied followed by spectral analysis of satellite imagery to identify candidate parcels for truck parking facilities within urban areas. The automated processes created a ranked short list of potential parcels from which those best suited for truck parking could be efficiently identified for inspection by satellite imagery. This process resulted in a manageable number of parcels to be evaluated further by local knowledge metrics such as availability and cost, existing infrastructure and municipal connections, and safety.

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