Wetlands and Remote Sensing
Wetlands are not Waste-lands
Wetlands, among the most important ecosystems in the world, are commonly known as "marshes" or "swamps", and placed in a negative context by the general public. Wetlands are perceived as breeding grounds for mosquitos, bad smelling and polluted. Wetlands are considered dangerous to human health and as an impediment to progress (Mitsch and Gosslink, 1993). Due to these common mis-perceptions and lack of education, people fail to acknowledge the role that wetlands play as habitat for important flora and fauna, as pollution and sediment sinks, as natural flood control mechanisms, and their help to maintain water levels. Wetlands are also of considerable scientific interest because they are associated with important ecosystem functions and processes, useful and economically viable products, and biological diversity. For all above mentioned and other reasons, there has been a large scale destruction of these productive ecosystem.
An Overview of Nebraska Wetlands
The Nebraska Wetlands Priority Plan prepared by Gerbis (1991) for Nebraska Game and Park Commission divides wetlands of Nebraska into four categories. These are playas, sandhills, saline/alkaline, and riverine. These wetlands occur throughout the state. The sandhill wetlands complex is the biggest one in all the for categories covering an area of about 1,307,000 acres. Whereas, rainwater basin of playa wetlands is the second largest category with a total area of around 34, 103 acres.
According to Dahl (1990), there were 2,910,000 acres of wetlands in Nebraska at the time of statehood (1867). This is approximately 6% of the total area of the state. A 35% loss in wetlands area since 1867 shrunk them to 1, 905, 000 acres. Which is only 3.9% of the total area.
The Nebraska Sand Hills region is comprised mainly of vast areas of grassland, open water and wetlands (Miller, 1990). This area has a number of variable size wetlands which range from less than an acre to 2,300 acres with majority of them are around 10 acres or less (Wolf, 1984). The formation of most of the open water lakes and wetlands in the region is due to the large underground reservoir, known as Ogallala Aquifer, which provides a water table at or very close to the surface (LaGrange, 1997).
Remote Sensing and Wetlands
Remote sensing has provided a great mean to study various ecosystems of the earth including wetlands by providing cost and time effective data. Over the years, remote sensing has been used as a tool to map large areas of wetlands. Moreover, remote sensing in the form of aerial photography served the purpose of identification, delineation and measurement of spatial extent of wetland successfully (e.g., National Wetland Inventory (NWI) ) (Reimold et al, 1973).With regular passages of remote sensing vehicles (aircraft and/or satellites) over a locality, land information in the form of multi-date, multi-spectral images can be obtained within a constant period of time. Changes in surface environmental conditions can therefore be monitored using space-borne digital imageries. With launch of remote sensing satellites like the Landsat series with Multispectral Scanner (MSS) and later Thematic Mapper (TM), it has become cost effective and convenient to acquire multi-date digital images over a greater array of spatial and temporal scales than was possible with aerial photography. Landsat-MSS has been used successfully for the study of relatively larger wetlands ( Klemas et al, 1975, Work and Gilmer 1976, Gilmer et al, 1980, Jensen et al, 1986, and other ). Yet, its use is very limited for studies of different aspects (e.g., vegetation species identification, discrimination, etc.) of all types of wetlands, specifically, inland wetlands which usually have smaller areal extent and complex mixture of vegetation species. Basic constraints of using Landsat-MSS data for wetland mapping inventory in early studies were geometric inaccuracy and the poor spatial, spectral, and radiometric resolutions of data (Carter, 1982). Availability of Landsat-TM data solved this problem of coarse resolutions to some extent. With a spatial resolution of 30 meters, it becomes possible to study relatively smaller areas (Dottavio and Dottavio, 1984, Adeniyi et al, 1985).
|TM July 16, 1986||TM July 14, 1991||TM April 27, 1992|
Digital Change Detection Techniques
One major and wide spread use of remotely sensed data has been easier, faster, and cost effective investigation of various types of changes in different environments. For example, remote sensing based change detection has been used to monitor and control urban development, assess and monitor deforestation, and improve agricultural yields by detecting early crop stresses and diseases, etc. However, wetland environment is an exception. Although, wetlands are among the most productive and important ecosystems of the world, few studies using remote sensing data have been conducted to monitor changes in wetlands especially, inland freshwater wetlands (Carter 1977, Wickware and Howarth 1981, Frick 1984, and others).
There are several digital change detection algorithms or techniques which have been developed and used over the years to estimate changes using remote sensing (in most cases satellite) data. These techniques are based on various mathematical and/or statistical relationships, principles and assumptions. The use of one specific change detection technique or method over another can calculate a significantly different estimate of the change for a same area. Therefore, it is important to use the most appropriate technique to study a particular area and environment.
To explain the application of digital change detection techniques here I give one example. An overall aim of this study was to detect and map changes in the areal extent of standing surficial water and wetlands over time, several standard, commonly used image-analysis techniques were considered. Out of all the techniques Tasseled Cap Transformation (TCT ) was selected to use for the study. TCT is a method to convert an input image acquired by a multispectral sensor (i.e., one operating simultaneously in several spectral regions such as visible green, visible red, and near-infrared) into an image which has three main output components that highlight feature classes such as bare soil, vegetation, and water (Kauth and Thomas 1976, Crist and Kauth 1986, and Crist 1985)
|TCT July 16, 1986||TCT July 14, 1991||TCT April 27, 1992|
The calculation of TCT, which is a complex procedure involving the linear combination of all the bands of an image, results in six synthetic output channels for a Landsat-TM image, where band 1 corresponds to soil brightness, band 2 to vegetation greenness, band 3 to wetness, band 4 to haze in the atmosphere, and bands 5 and 6 to system noise. Trial and error confirmed, for Brown County, that a combination of first two transformed bands provided a best result for separating vegetation, soil and water. The two transformed bands (synthetic channels) were then used to classify the image subset for the study area into three general classes (soil, vegetation, and water).
|July 16, 1986||July 14, 1991||April 27, 1992|
These classes were then labeled, evaluated, and verified by comparing them to both black-and-white and color-infrared aerial phonographs, and also to NWI results. Areas of surficial water and wetlands in and around each lake in the study area were then calculated. The same procedure was applied for all three TM images (1986, 1991, and 1992).
|Lake areas, 1986||Lake areas, 1991||Lake areas, 1992||Wetland areas, 1986||Wetland areas, 1991||Wetland areas, 1992|