Remote sensing of emergent and submerged wetlands: an overview

被引:104
作者
Klemas, V. [1 ]
机构
[1] Univ Delaware, Sch Marine Sci & Policy, Newark, DE 19716 USA
关键词
CHANGE VECTOR ANALYSIS; SEA-LEVEL RISE; AQUATIC VEGETATION; AIRBORNE LIDAR; COASTAL WETLANDS; LAND-COVER; C-BAND; HYPERSPECTRAL IMAGERY; CHESAPEAKE BAY; SPECTRAL DISCRIMINATION;
D O I
10.1080/01431161.2013.800656
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
To plan for wetland protection and responsible coastal development, scientists and managers need to monitor changes in the coastal zone, as the sea level continues to rise and the coastal population keeps expanding. Advances in sensor design and data analysis techniques are now making remote-sensing systems practical and cost-effective for monitoring natural and human-induced coastal changes. Multispectral and hyperspectral imagers, light detection and ranging (lidar), and radar systems are available for mapping coastal marshes, submerged aquatic vegetation, coral reefs, beach profiles, algal blooms, and concentrations of suspended particles and dissolved substances in coastal waters. Since coastal ecosystems have high spatial complexity and temporal variability, they should be observed with high spatial, spectral, and temporal resolutions. New satellites, carrying sensors with fine spatial (0.4-4 m) or spectral (200 narrow bands) resolution, are now more accurately detecting changes in coastal wetland extent, ecosystem health, biological productivity, and habitat quality. Using airborne lidars, one can produce topographic and bathymetric maps, even in moderately turbid coastal waters. Imaging radars are sensitive to soil moisture and inundation and can detect hydrologic features beneath the vegetation canopy. Combining these techniques and using time-series of images enables scientists to study the health of coastal ecosystems and accurately determine long-term trends and short-term changes.
引用
收藏
页码:6286 / 6320
页数:35
相关论文
共 258 条
[1]   Airborne laser scanning - present status and future expectations [J].
Ackermann, F .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 1999, 54 (2-3) :64-67
[2]   REMOTE-SENSING OF SUBMERGED AQUATIC VEGETATION IN LOWER CHESAPEAKE BAY - A COMPARISON OF LANDSAT MSS TO TM IMAGERY [J].
ACKLESON, SG ;
KLEMAS, V .
REMOTE SENSING OF ENVIRONMENT, 1987, 22 (02) :235-248
[3]   Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review [J].
Adam, Elhadi ;
Mutanga, Onisimo ;
Rugege, Denis .
WETLANDS ECOLOGY AND MANAGEMENT, 2010, 18 (03) :281-296
[4]   Application of spherical statistics to change vector analysis of Landsat data: Southern Appalachian spruce-fir forests [J].
Allen, TR ;
Kupfer, JA .
REMOTE SENSING OF ENVIRONMENT, 2000, 74 (03) :482-493
[5]   A simple method of measuring beach profiles [J].
Andrade, Francisco ;
Ferreira, Maria Adelaide .
JOURNAL OF COASTAL RESEARCH, 2006, 22 (04) :995-999
[6]   Remote sensing:: a key tool for interdisciplinary assessment of coral reef processes [J].
Andréfouët, S ;
Riegl, B .
CORAL REEFS, 2004, 23 (01) :1-4
[7]  
[Anonymous], 2007, INT HYDROGR REV
[8]  
[Anonymous], ENCY COASTAL SCI
[9]  
[Anonymous], ORBVIEW 3 SAT GROUND
[10]  
[Anonymous], 14 INT C APPL GEOL R