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Coastline change detection using remote sensing

December 2007
International journal of Environmental Science and Technology 4(1)

DOI:10.1007/BF03325962

Authors:

Ali Asghar Alesheikh

K. N. Toosi University of Technology

Ali Ghorbanali

Islamic Azad University, Estahban Branch

Nasim Nouri

Coast is a unique environment in which atmosphere, hydrosphere and lithosphere contact each other. Coastline is one of the most important linear features on the earth’s surface, which display a dynamic nature. Coastal zone, and its environmental management requires the information about coastlines and their changes. This paper examines the current methods of coastline change detection using satellite images. Based on the advantages and drawbacks of the methods, a new procedure has been developed. The proposed procedure is based on a combination of histogram thresholding and band ratio techniques. The study area of the project is Urmia Lake; the 20th. largest, and the second hyper saline lake in the world. In order to assess the accuracy of the results, they have been compared with ground truth observations. The accuracy of the extracted coastline has been estimated as 1.3 pixels (pixel size=30 m). Based on this investigation, the area of the lake has been decreased approximately 1040 square kilometers from August 1998 to August 2001. This result has been verified through TOPEX/Posidon satellite information that indicates a height variation of three meters.

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A. A. Alesheikh, et al.

Coastline change detection using remote sensing

A. A. Alesheikh,

A. Ghorbanali,

N. Nouri

Department of GIS Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran

Department of Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran

Departmentof Environmental Engineering,

Graduate School of the Environment and Energy,

Science and Research Campus, Islamic Azad University, Tehran, Iran

Received 1 October 2006; revised 20 November 2006; accepted 1 December 2006; available online 1 January 2007

*Corresponding author, Email: alesheikh@kntu.ac.ir

Tel: +9821 8877 0006; Fax: +9821 8877 9476

ABSTRACT:

Coast is a unique environment in which atmosphere, hydrosphere and lithosphere contact each other.

Coastline is one of the most important linear features on the earth’s surface, which display a dynamic nature. Coastal

zone, and its environmental management requires the information about coastlines and their changes. This paper

examines the current methods of coastline change detection using satellite images. Based on the advantages and

drawbacks of the methods, a new procedure has been developed. The proposed procedure is based on a combination

of histogram thresholding and band ratio techniques. The study area of the project is Urmia Lake; the 20

th.

largest, and

the second hyper saline lake in the world. In order to assess the accuracy of the results, they have been compared with

ground truth observations. The accuracy of the extracted coastline has been estimated as 1.3 pixels (pixel size=30 m).

Based on this investigation, the area of the lake has been decreased approximately 1040 square kilometers from August

1998 to August 2001. This result has been verified through TOPEX/Posidon satellite information that indicates a

height variation of three meters.

Key words:

Coastline extraction, TM & ETM+ sensors, histogram thresholding, band ratios, remote sensing

INTRODUCTION

Coastal zone monitoring is an important task in

sustainable development and environmental

protection. For coastal zone monitoring, coastline

extraction in various times is a fundamental work.

Coastline is defined as the line of contact between land

and the water body. Coastline is one of the most

important linear features on the earth’s surface, which

has a dynamic nature (Winarso, et al., 2001). Remote

sensing plays an important role for spatial data

acquisition from economical perspective (Alesheikh,

et al., 2003). Optical images are simple to interpret and

easily obtainable. Further more, absorption of in frared

wavelength region by water and its strong reflectance

by vegetation and soil make such images an ideal

combination for mapping the spatial distribution of land

and water. These characteristics of water, vegetation

and soil make the use of the images that contain visible

and infrared bands widely used for coastline mapping

(DeWitt, et al., 2002). Examples of such images are: TM

(Thematic Mapper) and ETM+ (Enhanced Thematic

Mapper) imagery (Moore, 2000). Coastline change

mapping for Urmia Lake by TM and ETM+ imagery is

the main aim of this paper. Furthermore, a new semi-

automatic approach for coastline extraction from TM

and ETM+ imagery has been developed and presented.

History

From 1807 to 1927, all coastline maps have been

generated through ground surveying. In 1927 the full

potential of aerial photography to complement the

coastline maps was recognized. From 1927 to 1980,

aerial photographs were known as the sole source for

coastal mapping. However, the number of aerial

photographs required for coastline mapping, even at a

regional scale, is large (Lillesand, et al., 2004).

Collecting, rectifying, analyzing and transferring the

information from photographs to map are costly and

time consuming. In addition to cost, using black and

white photographs creates several other problems.

First, the contrast between the land and water in the

spectral range of panchr omatic photographs is minimal,

particularly for the turbid or muddy water of coastal

Int. J. Environ. Sci. Tech., 4 (1): 61-66, 2007

ISSN: 1735-1472

IRSEN, CEERS, IAU

A. A. Alesheikh, et al.

region, and the interpretation of the coastline is difficult

(De Jong and Van Der Meer, 2004). Second, the

photographs and the resultant maps are in a non-digital

format, reducing the versatility of the data set. Labor

intensive digitization is required to transfer the

information to a digital format, and this process

introduces additional costs and errors. The geometric

complexity and fragmented patterns of coastlines

compounds these problems. In addition to the above,

other possible limitations are: (1) the lack of timely

coverage, (2) the lack of geometrical accuracy unless

ortho-rectified, (3) the expense of the analytical

equipment, (4) the intensive nature of the procedure

(Miao and Clements, 2002), and (5) the need for skilled

personnel. In addition to high costs and difficulties,

generation of coastline maps has fallen sadly out of

date. From 1972 the Landsat and other remote sensing

satellites provide digital imagery in infrared spectral

bands where the land-water interface is well defined.

Hence remote sensing imagery and image processing

techniques provide a possible solution to some of the

problems of generating and updating the coastline

maps (Winarsoet, et al., 2001).

MATERIALS AND METHODS

Study Area

The study site of this investigation is Urmia Lake.

The lake is located between latitude 37°N to 38.5°N

and longitude 45°E to 46°E. Urmia Lake is the 20th

largest and the second hypersaline lake in the world.

The Urmia Lake covers an average area of 5,100 square

kilometers. The maximum and average depth of this

lake are 16 and 5 meters, respectively. Urmia Lake is

listed as a biosphere reserve by UNESCO (United

Nation Education, Scientific and Cultural Organization)

(Birkett, et al., 1995). Also, it is recognized as a national

park. The digital images used in this research are: three

Landsat 7 ETM+ images; three Landsat 5 TM images;

three Landsat 4 TM images. The following Table shows

the spectral and spatial characteristics of Landsat 7

ETM+ and Landsat 5 TM sensors. For this experiment

ENVI V3.5 and ERMapper software are used for all the

image processing needs.

Methodology

Various methods for coastline extraction from optical

imagery have been developed. Coastline can even be

extracted from a single band image, since the

reflectance of water is nearly equal to zero in reflective

infrared bands, and reflectance of absolute majority of

landcovers is greater than water. This can be achieved,

for example, by histogram thresholding on one of the

infrared bands of TM or ETM+ imagery. Experience

has shown that of the six reflective TM bands, mid-

infrared band 5 is the best for extracting the land-water

interface (Kelley, et al., 1998). Band 5 exhibits a strong

contrast between land and water features due to the

high degree of absorption of mid-infrared energy by

water (even turbid water) and strong reflectance of mid-

infrared by vegetation and natural features in this

range. Of the three TM infrared bands, band 5

consistently comprises the best spectral balance of

land to water. The dynamic and complex land-water

interaction in coastal Urmia Lake wetlands makes the

discrimination of land-water features less certain,

especially in marsh environments (Ghorbanali, 2004).

The histogram of TM band 5 ordinarily displays a sharp

double peaked curve, due to tiny reflectance of water

and high reflectance of vegetation (Chen, 2003). The

transition zone between land and water resides between

the peaks. The transition zone is the effect of mixed

pixels and moisture regimes between land and water. If

the reflectance values are sliced to two discrete zones,

they can be depicted water (low values) and land

(higher values). But the difficulty of this method is to

find the exact value, as any threshold value will be

exact on some area, not all. Another method is to use

the band ratio between band 4 and 2 and also, between

band 5 and 2. With this method water and land can be

separated directly.

Table 1: Landsat 7 ETM+ and landsat 5TM spectral and spatial resolution

Band No. Spectral range (Microns) ETM+/TM Ground resolution (m)

ETM+/TM

1 .45 to .515 / .45 to .52 30

2 .525 to .60 / .52 to .60 30

3 .63 to .69 / .63 to .69 30

4 .75 to .90 / .76 to .90 30

5 1.55 to 1.75/1.55 to 1.75 30

6 (L/H) 10.4 to 12.5/10.5 to 12.4 60 / 120

7 2.09 to 2.35/2.08 to 2.35 30

Pan .52 to 90 / Nil 15 / Nil

A. A. Alesheikh, et al.

Fig. 1: Flowchart of extracting coastlines from images

The ratio b2/b5 is greater than one for water and

less than one for land in large areas of coastal zone.

ERMapper software uses this ratio as an algorithm for

sepa rat ing wa ter fr om lan d from TM or ETM+ imagery.

This law is exact in coastal zones covered by soil, but

not in land with vegetative cover. Actually, this law

mistakenly assigns some of the vegetative lands to

water. To solve this problem, the two ratios are

combined in this investigation. Applying this method,

the coastline can be extracted with higher accuracy.

But the problem occurs in some of the coastal zones

(i.e. in some areas, the coastline moves toward water).

If the aim is rapid coastline extraction, then it is a

supreme method. But when the aim is accurate coastline

extraction, then it is not a fine method. To solve this

problem, two techniques exist. In the first technique, a

color composite can be used for editing the coastline

map. The best color composite for this technique is

RGB (Red Green Blue) 5 43. Th is color composite n icely

depicts water-land interface. Furthermore, it is very

similar to the true-color composite of earth’s surface.

Moreover, it includes the bands that have low

correlation coefficient, and therefore, it contains higher

information in comparison to other color composites

(Moore, 2000). However, this technique is time

consuming and needs a lot of editing. In the second

technique, histogram thresholding method is used on

band 5 for separating land from water. The threshold

values have been chosen such that all water pixels are

classified as water, and most of land pixels have been

classified as land. In this case, few land pixels

mistaken ly have been assigned to water pixels but not

vice versa. Water pixels are then assigned to one and

land pixels to zero. Therefore, a binary image has been

achieved. This image is named “image No. 1”. The

image obtained from band ratio technique, also labels

water pixels to one and land pixels to zero. This second

image is named “image No. 2”. Then the two images

are multiplied. The final obtained binary image

represents the coastline accurately. Fig. 1 illustrates

the steps of the developed technique.

RESULTS

To evaluate the accuracy of this approach, it is

required to compare the extracted coastline with the

extracted coastline from a ground truth map. Because

of the lack of a reliable ground truth map, an image-

driven reference data is utilized (Alesheikh, et al., 1999).

The ground truth image was provided via fusing the

ETM+ multispectral bands with ETM+ panchromatic

band. Then, the coastline from the ground truth image

is extracted via visual interpretation.

Int. J. Environ. Sci. Tech., 4 (1): 61-66, 2007

Histogram thresholding

on band 5

Radiometric calibration

TM and ETM+

Applying the b2/b4>1 and b2/b5>1

conditions on images

Image N o. 1 Image No. 2

Multiplying 2 images

Final binary

Raster to vector

Coastline map

A. A. Alesheikh, et al.

Fig. 2: Urmia Lake in Aug-1998 color composite

RGB (543) Fig. 3: Urmia Lake in Jun-1989 color composite

RGB (543)

Fig. 4: Urmia Lake in Aug-2001

color composite RGB (543)

Fig. 5: The map of shorelines

Coastline change detection using remote...

2001

1998

1989

Map of Shorelines

A. A. Alesheikh, et al.

(m)

(years)

Next the two mentioned coastline data are compared,

and the accuracy of the extracted coastline was

estimated as 1.3 pixels (pixel size=30meters). Figs. 2, 3

and 4 illustrate mosaicked images of Urmia Lake in the

years 1989, 1998 and 2001 respectively. These images

are color composite RGB 543. Fig. 5 illustrates the

coastline change map for Ur mia La ke. Fig . 5 sh ows tha t

the area of Urmia Lake in 1998 and 2001 equaled to

5650 and 4610 square kilometers respectively.

Therefore, the area of this lake has been decreased

approximately 1040 square kilometers from August 1998

to August 2001.

DISCUSION AND CONCLUSION

To compare the coastline map to Urmia Lake level

variations, the obtained information from TOPEX/

POSEIDON satellite has been used. This satellite is in

orbit at a height of 1330 km, and sends back

information that measures the average ocean height

very accurately (Jupp, 1988). Fig. 6 illustrates the

relative lake height variations computed from TOPEX/

POSEIDON satellite data. Because of the lack of

TOPEX/POSEIDON data from 1989 to 1993, four

points have been drawn in these years. These four

points represent the height of water that has been

measured by dipstick (Fig. 6). Fig. 6 illustrates that

the lake level variation equals 0.2 meters, from Junuary

1989 to August 1998, which is small in comparison to

seasonal level variations of this lake. But the lake

level variation equals to 3 meters from August 1998 to

August 2001, approximately. It is necessary to mention

that the tide-range in Urmia Lake is inconsiderable.

Therefore, the lake level variations and coastline

changes are not affected by tide. In fact, these

changes derive from water balance of Urmia Lake.

Several methods have been devised to detect the

coastline changes. Among them, remote sensing

appears to be a cost effective way. Histogram

thresholding is inadequate in depicting the real

changes especially in marsh area, as any threshold

value will be exact only on some area. Using the band

ratio between band 4 and 2, and also, between band 5

and 2 can result in water-land discrimination.

However, the method mistakenly assigns some of the

vegetative lands to water. Based on the evaluation,

and the dynamic and complex land-water interaction

in coastal Urmia Lake, a new procedure has been

developed and presented in this paper. In the new

approach, histogram thresholding and band ratio

techniques are used together. The results have been

evaluated using ground truth image-driven data and

showed superior accuracy, namely; 1.3 pixels (pixel

size=30 m). Topex/Posidon satellite information can

be used to determine water level variations. Using

such data for Urmia Lake showed three meters decrease

in the lake level from August 1998 to August 2001. It

has caused approximately 1000 square kilometers

decrease in the area of Urmia Lake. The lake level

variations and coastline changes are not affected by

tide, as tide-range in Urmia Lake is inconsiderable. In

this research, Urmia Lake coastlines have been

extracted from ETM+ and TM imagery. The coastline

map illustrates that the shoreline has small changes

from 1989 to 1998 year, and great changes from August

1998 to August 2001. The small changes in Urmia Lake

coastline are perpetual. The great changes have

happened as the result of 3 m decrease in the height

of water of Urmia Lake.

Fig. 6: Urmia Lake Level variations from 1989 to 2001

Int. J. Environ. Sci. Tech., 4 (1): 61-66, 2007

A. A. Alesheikh, et al.

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This article should be referenced as follows:

Alesheikh, A.A., Ghorbanali, A., Nouri, N., (2007). Coastline change detection using remote sensing. Int. J.

Environ. Sci. Tech., 4 (1), 61-66.

AUTHOR (S) BIOSKETCHES

Alesheikh, A.A., Assistant professor, Department of GIS Engineering, Khaje Nasir Toosi University of

Technology, Tehran, Iran. Email: alesheikh@kntu.ac.ir

Ghorbanali, A., Department of Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran,

Iran. Email: alighorbanali@yahoo.com

Nouri, N., M.Sc. Student, Departmentof Environmental Engineering,

Graduate School of the Environment and

Energy, Science and Research Campus, Islamic Azad University, Tehran, Iran. Email: nourinahal@yahoo.com

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Agriculture is one of the most important economic sectors in Vietnam, however, in recent times, agricultural production has been negatively affected by drought, reducing crop productivity and quality. Due to the effects of climate change, drought occurs in most regions of the country with varying degrees and duration, seriously affecting water resources and agricultural production. Particularly for the Central Highlands region, drought is a natural disaster with the most negative impacts on life and production. This paper presents the results of monitoring the changes in water surface area of some reservoirs in Dak Lak province in the dry season in early 2020 from Sentinel 2 data. The MNDWI index calculated from green and NIR band of Sentinel 2 images is used to extract surface water, and thereby evaluating the change in surface water area of the reservoirs. The obtained results show a very strong decrease in water surface area of reservoirs in Dak Lak due to the influence of drought. The water surface area of Ea Sup Thuong Lake decreased about 6 times at the end of the dry season (May 2020) compared to the period of January 2020. The water surface area of Ea Uy Lake decreased about 4 times, while with the Krong Buk Ha Lake, the decrease in water surface area was lower, reaching about 28% compared to the beginning of the dry season. The results obtained in the study provide timely information to help managers effectively respond to the effects of drought on water resources.

Examination and Meteorological Evaluation of the Temporal Changes of the Water Surface of Burdur Lake with the Support of Remote Sensing and GIS

Chapter

Oct 2024

Lakes, sustaining their existence depending on water sources such as groundwater, rivers, and precipitation, constitute an important part of the water cycle. Additionally, lakes contribute to the examination of issues such as water quality, ecosystem health, and the impacts of climate change. Climate change and environmental threats can affect the water levels of lakes, hence the vital importance of preserving and sustainably managing these ecosystems. Protecting lakes promotes sustainable use of water resources and helps preserve clean and abundant water sources for future generations. Monitoring water resources is of great importance for planning, sustainable development, and change analyses. Today, thanks to advancing technology, monitoring of water resources can be successfully carried out using remote sensing (RS) data and Geographic Information Systems (GIS). The aims of this study are to examine the surface area change of Burdur Lake in detail, analyze it with RS and GIS techniques, and create climate boundary maps to understand whether it is associated with drought. For this purpose, the change in the lake surface area for each year between 1993 and 2023 in the study area has been determined. According to the results of this study, it was determined that Burdur Lake lost approximately 35.35% of its the area at the surface over a span of 30 years.

THE POTENTIAL APPLICATION REMOTE SENSING DATA FOR COASTAL STUDY

Article

Full-text available

Jan 2001

Coastal is an area that is influent by two processing factors that are marine and land dynamic process. In this area, occurs a complex dynamic process, which caused the relatively quick changes. The use of remote sensing data to study the process that occur in the area will get some helpful information to know the changes that happened, e.g. to study coastline dynamic. The need of this kind information is useful by countries that have long coastline and have so many islands just like Indonesia. This study use Landsat 7 data with ETM+ sensor, supported by Landsat 5 (TM), bathymetric map and tidal data from the study case area of Riau Archipelago especially Batam Island and surrounding. From this study can be summarize that the information on the coastline can be obtain in easy and accurate with combining band ratio of 4/2 and 5/2. The result has to be corrected with visual interpretation of color composite 543 RGB by visual editing to reduce errors from digital processing. The result also has to be corrected with tidal data because the information are obtain from different tidal time, which are usually different with the standard tidal data that are used to determine the coastline. Preface

Remote Sensing Image Analysis: Including The Spatial Domain

Book

Jan 2004

Remote Sensing image analysis is mostly done using only spectral information on a pixel by pixel basis. Information captured in neighbouring cells, or information about patterns surrounding the pixel of interest often provides useful supplementary information. This book presents a wide range of innovative and advanced image processing methods for including spatial information, captured by neighbouring pixels in remotely sensed images, to improve image interpretation or image classification. Presented methods include different types of variogram analysis, various methods for texture quantification, smart kernel operators, pattern recognition techniques, image segmentation methods, sub-pixel methods, wavelets and advanced spectral mixture analysis techniques. Apart from explaining the working methods in detail a wide range of applications is presented covering land cover and land use mapping, environmental applications such as heavy metal pollution, urban mapping and geological applications to detect hydrocarbon seeps. The book is meant for professionals, PhD students and graduates who use remote sensing image analysis, image interpretation and image classification in their work related to disciplines such as geography, geology, botany, ecology, forestry, cartography, soil science, engineering and urban and regional planning.

External knowledge in image classification: How to improve classification accuracy

Article

Aug 2003

Classification is a common technique for the extraction of information from remotely sensed images. Among several techniques, Maximum Likelihood Classification (MLC) has been the most frequently used. Standard methods such as this involve pixel values as fundamental elements and try to label these based on their spectral properties. The use of spectral properties alone may not, however, lead to adequate accuracy. The author aims to identify various errors in remotely sensed images and to reduce these using external knowledge, such as priori information about the expected distribution of classes in a final classification map. Validation results of this method showed a 30 per cent improvement in the overall accuracy.

Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters

Article

Jan 1989

D.L.B. Jupp

The use of passive remote sensing to map and monitor shallow coastal and reef waters is a significant opportunity which has been exploited in various ways over the past ten years. This paper outlines a set of algorithms which have emerged as useful and practical from work done in the coastal areas of Queensland and around the reefs of the Great Barrier Reef of Australia. The algorithms rely at base on determining the differing depths to which light penetrates in different wavebands and using the physics of this "depth of penetration' to map water depths, water properties and the type of material on the sea floor. -Author

Shoreline Mapping Techniques

Article

Dec 2000

L. J. Moore

Numerous coastal mapping techniques have been developed over the last twenty-seven years (STAFFORD, 1971; DOLAN et al., 1978; FISHER and SIMPSON, 1979; LEATHERMAN, 1983; McBRIDE et al., 1991; THIELER and DANFORTH, 1994a, OVERTON et al., 1996). These techniques, used to measure shoreline erosion, barrier island migration, and dune erosion, vary in approach, accuracy, expense and training/time requirements. Some of the more recent coastal mapping techniques apply advances in cartography and photogrammetry providing high-resolution measurements with less error than manual methods that use a photographic comparator or stereo zoom transfer scope. However, such techniques are expensive, require extensive training, and may take longer than manual methods. While many coastal mapping studies would benefit from these advanced techniques, not all studies require the high resolution these more recent techniques offer. When beginning a coastal mapping project or choosing to upgrade laboratory facilities, researching established coastal mapping techniques before choosing from among them requires extensive literature review. To assist researchers, engineers and planners who wish to undertake a coastal mapping project, this paper provides an overview of the errors associated with shoreline mapping, and a discussion of factors to be considered when selecting a coastal mapping technique.

Generation of Three-Dimensional Lake Model Forecasts for Lake Erie

Article

Sep 1998

A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semioperational versions of the Pennsylvania State University-National Center for Atmospheric Research three-dimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The coupled system was tested using archived MM4 36-h forecasts for three cases during 1992 and 1993. The cases were chosen to demonstrate and evaluate the forecasts produced by the coupled system during severe lake conditions and at different stages in the lake's annual thermal cycle. For each case, the lake model was run for 36 h using surface heat and momentum fluxes derived from MM4's hourly meteorological forecasts and surface water temperatures from the lake model. Evaluations of the lake forecasts were conducted by comparing forecasts to observations and lake model hindcasts. Lake temperatures were generally predicted well by the coupled system. Below the surface, the forecasts depicted the evolution of the lake's thermal structure, although not as rapidly as in the hindcasts. The greatest shortcomings were in the predictions of peak water levels and times of occurrence. The deficiencies in the lake forecasts were related primarily to wind direction errors and underestimation of surface wind speeds by the atmospheric model. The three cases demonstrated both the potential and limitations of daily high-resolution lake forecasts for the Great Lakes. Twice daily or more frequent lake forecasts are now feasible for Lake Erie with the operational implementation of mesoscale atmospheric models such as the U.S. National Weather Service's Eta Model and Rapid Update Cycle.

Remote Sensing and Image Interpretation

Book

Jan 1987

The author's introduction to remote sensing provides coverage of the subject irrespective of disciplines of study or the academic department in which remote sensing is taught. All the ''classical'' elements of aerial photographic interpretation and photogrammetry are described, but equal emphasis is placed on non-photographic sensing systems and the analysis of data from these systems using digital image processing procedures. This text includes coverage of image restoration, enhancement, classification, and data merging, and new sensor systems such as the Large Format Camera, solid-state linear arrays, the Shuttle Imaging radar systems, the Landsat Thematic Mapper, the SPOT satellite system, and the NOAA Advanced Very High Resolution Radiometer. Also covers imaging spectrometry and lidar systems. It contains extensive illustrations.

A New Global Lakes Database for a Remote Sensing Program Studying Climatically Sensitive Large Lakes

Article

Dec 1995
J GREAT LAKES RES

World wide climate related programs have called for hydrological studies to be put into a more global perspective, and yet the global study of large climatically sensitive lakes has been neglected. A remote sensing program at the Mullard Space Science Laboratory (MSSL) aims to monitor short and medium-term lake volume changes and interpret them in terms of aridity variations, as a measure of regional climate change. The program thus requires the global identification of all potentially closed and climatically sensitive lakes. We review the availability of current global lake (particularly closed lake) information and note its limitations with respect to the requirements of the large lakes study. As a result of this the MSSL Global Lakes Database (MGLD) has been constructed containing locational and lake type information for over 1,400 inland water bodies including, as far as possible, all lakes and reservoirs (but not lagoons), with surface areas ≥ 100km2 (approximately). Of these, the database identifies 857 open lakes (those with outflows), 226 reservoirs, and 320 potentially closed lakes. Approximate areas of the lakes are also noted. The identification of the closed lakes (59% on the Asian continent) is a cornerstone of the remote sensing program which aims to derive lake levels and lake areas using satellite radar altimeters and satellite imaging radiometers respectively. The MGLD therefore also notes the spatial coverage of all large lakes by past and current radar altimeters including that on ERS-1, which overflies almost half of the lakes. With the addition of referenced closed-lake information the MGLD is thus a unique data set which has already been applied to several remote sensing projects.

Remote Sensing and Image Interpretation

Article

Feb 1979

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Trends on information processing for remote sensing

Conference Paper

Sep 1997

C.H. Chen

There has been greatly increased activity in the last twelve years on the use of information processing techniques on remote sensing problems including signal/image processing, compression, segmentation, feature extraction, pattern recognition, neural networks, etc. The past progress is reviewed from which a trend is developed. The trend shows a further emphasis on using neural networks and wavelet transforms for remote sensing