Scale Management and Remote Sensor Synergy in Forest Monitoring
Bibliographic Data
| ID | 5877568 |
|---|---|
| Authors | Jason A Tullis (0000-0002-1134-4373), Jason M Defibaugh Y Chávez (University of Arkansas at Fayetteville) |
| Year | 2009 |
| Volume | 3 |
| Issue | 1 |
| Pages | 154-170 |
| Publication date | 2009-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Geography Compass (JOURNAL) |
| Journal identifiers | ISSN: 1749-8198 • E-ISSN: 1749-8198 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1749-8198.2008.00191.x |
| OpenAlex | W2078798784 |
| Language | EN |
| References cited | 33 |
Quickened remote sensing-assisted data-to-decision pathways in forest monitoring and other applications are fragmented with respect to the treatment of spatial scale. While definitions, terminology, and fundamental scale questions are fairly stable, common practices of dealing with the effects of spatial scale in such applications as forest monitoring have remained stagnant for decades. Recent studies are searching for ways to efficiently manage scale so that remote sensing-derived information factors such as reliability and economy may be maximized. Development and research in emerging technologies such as artificial intelligence-assisted spatial data processing, high-throughput computing, automated geometric correction, and object-oriented image analysis can be positioned so as to facilitate significantly improved worldwide forest remote sensor synergy. The potential impact of research in automated spatial scale management to this end suggests that it should be computationally integrated with forest remote sensing applications
Cartography · Data mining · Data science · Environmental resource management · Geography · Remote sensing · Spatial analysis · Terminology · Computer Science · Environmental Science · Land Use and Ecosystem Services · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture
| Highly cited | No |
|---|