Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data

被引:65
作者
Clark, Matthew L. [1 ]
Aide, T. Mitchell [2 ]
机构
[1] Sonoma State Univ, Dept Geog & Global Studies, Ctr Interdisciplinary Geospatial Anal, Rohnert Pk, CA 94928 USA
[2] Univ Puerto Rico, Dept Biol, San Juan, PR 00931 USA
基金
美国国家科学基金会;
关键词
reference data collection; Google Earth; geoweb; land use/land cover; volunteer geographic information; crowdsourcing; MODIS; RESOLUTION; ACCURACY; IMAGERY;
D O I
10.3390/rs3030601
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Web-based applications that integrate geospatial information, or the geoweb, offer exciting opportunities for remote sensing science. One such application is a Web-based system for automating the collection of reference data for producing and verifying the accuracy of land-use/land-cover (LULC) maps derived from satellite imagery. Here we describe the capabilities and technical components of the Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT), a collaborative browser-based tool for "crowdsourcing" interpretation of reference data from high resolution imagery. The principal component of VIEW-IT is the Google Earth plug-in, which allows users to visually estimate percent cover of seven basic LULC classes within a sample grid. The current system provides a 250 m square sample to match the resolution of MODIS satellite data, although other scales could be easily accommodated. Using VIEW-IT, a team of 23 student and 7 expert interpreters collected over 46,000 reference samples across Latin America and the Caribbean. Samples covered all biomes, avoided spatial autocorrelation, and spanned years 2000 to 2010. By embedding Google Earth within a Web-based application with an intuitive user interface, basic interpretation criteria, distributed Internet access, server-side storage, and automated error-checking, VIEW-IT provides a time and cost efficient means of collecting a large dataset of samples across space and time. When matched with predictor variables from satellite imagery, these data can provide robust mapping algorithm calibration and accuracy assessment. This development is particularly important for regional to global scale LULC mapping efforts, which have traditionally relied on sparse sampling of medium resolution imagery and products for reference data. Our ultimate goal is to make VIEW-IT available to all users to promote rigorous, global land-change monitoring.
引用
收藏
页码:601 / 620
页数:20
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