Lesson 24 Effect of Land Use Land Cover on Watershed Hydrology

24.1 Methods of Watershed LU/LC Estimation

Land use land cover information has been of interest for planning resources as well as to understand the condition of the area since long. Earlier it was used to develop by surveying the area at regular interval of time. However, with the development of technology and growing need of getting reliable/accurate information within less time, new techniques have been developed and being employed. Nowadays, two technologically efficient methods are generally adopted for estimating land use/land cover of a watershed:

i)       Using Aerial Photographs

Aerial photographs have been used in the mapping of vegetation since 1920, but their development as a major tool in forestry and related fields has come in use since 1940. Modern techniques involving the use of aerial photographs have been made possible by the availability of high-grade photographs at a low cost, coupled with the development of simple photogrammetric instruments, and of photo-mensurational techniques. The principal advantage of aerial photographs in vegetation and land-use surveys lies in the fact that they provide a permanent record of conditions which is available in the office for detailed analysis. When studied with the aid of the stereoscope, they present a three-dimensional picture of the terrain seen directly from above. Maps can quickly and accurately be prepared from photographs. Units of vegetation and of land use can be delineated from the photographs and their areas determined with greater accuracy than is possible in the field in any reasonable amount of time.

ii)    Using Remote Sensing Imagery (RS)

Land cover mapping is one of the most important and typical applications of remote sensing data. Generally land cover does not coincide with land use. A land use class is composed of several land covers. Remote sensing data can better provide land cover information rather than land use information. Initially the land cover classification system should be established, which is usually defined as levels and classes. The level and class should be designed in consideration of the purpose of use (national, regional or local), the spatial and spectral resolution of the remotely sensing data, user's request and so on. Table 1 shows a classification typically used with remotely sensed data.

Table 24.1. Land Use and Land Cover Classification System Typically Used with Remotely Sensed Data



Urban or Built-up Land

Residential, commercial, services, transportation, communications utilities, industrial and commercial complexes, mixed urban or built-up land, other urban or built-up land

Agricultural Land

Cropland and pasture orchards, groves, vineyards, nurseries, and ornamental horticultural areas, confined feeding operations,  other agricultural land


Herbaceous rangeland, shrub and brush rangeland, mixed rangeland

Forest Land

Deciduous forest land, evergreen forest land, mixed forest land


Streams and canals, lakes, reservoirs, bays and estuaries


Forested wetland,  non-forested wetland

Barren Land

Dry salt flats, beaches, sandy areas, bare exposed rock, strip mines quarries, and gravel pits, transitional areas mixed barren land


Shrub and brush tundra, herbaceous tundra, bare ground tundra, wet tundra, mixed tundra

Perennial Snow/Ice

Perennial snowfields, glaciers

24.2 Use of Remote Sensing in LU/LC Estimation

Land use land cover classification can be carried out by using remote sensing images of required (available) resolution through the following process/steps.

i) Pre-processing

This includes data operations which normally precedes further manipulation and analysis of the image data to extract specific information. These operations aim to correct distorted or degraded image data to create a more faithful representation of the original scene. These preprocessing procedures are essential for ensuring high-quality information from remote sensors and are performed on satellite image data prior to the retrieval of land, atmosphere, and ocean information. Pre-processing functions are generally grouped as Radiometric or Geometric corrections.

Radiometric correction is important to ensure that terrestrial variables retrieved from optical satellite sensor systems are calibrated to a common physical scale. Radiometric correction ensures that measurements and methods yield self-consistent and accurate geophysical and biophysical data, even though the measurements are made with a variety of different satellite sensors under different observational conditions and the parameter retrieval methodologies. Radiometric corrections include correcting the data for Sensor Irregularities and Unwanted Sensor or Atmospheric Noise, and converting the data so that they accurately represent the reflected or emitted radiation measured by the sensor.

The images acquired by Earth observation systems cannot be transferred to maps as is, because they are geometrically distorted (spherical nature of land surface representation of 2D rectangular shape). These distortions are due to errors in the satellite’s positioning on its orbit, the fact that the Earth is turning on its axis as the image is being recorded, eg. the effects of relief. They are amplified even more by the fact that some satellites take oblique images. Some distortions, such as the effects of the Earth’s rotation and camera angles, are predictable. They thus can be calculated and correction values applied systematically. Satellites also have sophisticated on-board systems to record very slight movements affecting the satellite. This information is used mainly to correct the satellite’s position (when this is necessary), but can also be used to correct the images geometrically. The producers of satellite images generally propose applying the most elementary corrections based on the satellite’s known information.

ii) Collection of the Ground Truth Data

In order to "anchor" the satellite measurements, we need to compare them with something we have observed and measured. Ground truthing is one part of the calibration process where a person on the ground makes a measurement of the same thing the satellite is trying to measure, at the same time the satellite is measuring it. The two answers are then compared to help evaluate how well the satellite instrument is performing. Usually we believe the ground truth more than the satellite, because we have more experience making measurements on the ground and sometimes we can see what we are measuring with the naked eye.

iii) Image Classification

Objects of similar natures have similar spectral properties. That means that the electromagnetic radiation reflected by objects of the same nature is similar overall and these objects will thus have similar spectral signatures. Since the spectral signatures of the objects observed by satellites are converted into different colours in digital images, objects of the same kind will appear in closely related colours. This property has been used for years to interpret aerial photographs and the images supplied by Earth-observing satellites. The interpreter places in the same category all the objects in an image that seem to have the same or closely related colour. Since the colours in a digital image are merely a conventional transposition of numerical values, it is also possible to exploit the computer’s computational power to classify the pixels by their numerical values, which is to say, in the final analysis, by the corresponding objects’ spectral properties. This is the basic principle of image classification.

There are two types of image classification.

a) Unsupervised Classification

In unsupervised classification, the computer is allowed to analyze all of the spectral signatures of all of the image’s pixels and to determine their natural groupings, that is to say, to group the pixels on the basis of their similar spectral signatures. In some cases the user may impose the number of categories that he wants to have at the end of the classification process and in some programs can also force certain classes to appear. The classification algorithms usually involve several passes during which the proposed solutions are refined so as to create increasingly homogeneous and well-differentiated groups. The main advantage of this method is its great speed, for it requires practically no intervention from the user. Its main flaw is to be based exclusively on spectral differences, which do not always correspond to natural land cover categories. For example, unsupervised classification often yields several classes corresponding to grassy vegetation but only one class encompassing the entire urban fabric, roadways, and tilled fields, which does not usually meet the interpreter’s needs.

b) Supervised Classifications

Supervised classification is the procedure most often used for quantitative analysis of remote sensing image data. It rests upon using suitable algorithms to label the pixels in an image as representing particular ground cover types, or classes. A variety of algorithms is available for this, ranging from those based upon probability distribution models for the classes of interest to those in which the multi-spectral space is partitioned into class-specific regions using optimally located surfaces. Irrespective of the particular method chosen, the essential practical steps usually include:

  1. Decide the set of ground cover types into which the image is to be segmented (possible number of classes). These are the information classes and could, for example, be water, urban regions, croplands, rangelands, etc.

  2. Choose representative or prototype pixels from each of the desired set of classes. These pixels are said to form training data. Training sets for each class can be established using site visits, maps, air photographs or even photo-interpretation of a colour composite product formed from the image data. Often the training pixels for a given class will lie in a common region enclosed by a border. That region is then often called a training field.

  3. Use the training data to estimate the parameters of the particular classifier algorithm to be used; these parameters will be the properties of the probability model used or will be equations that define partitions in the multispectral space. The set of parameters for a given class is sometimes called the signature of that class.

  4. Using the trained classifier, label or classify every pixel in the image into one of the desired ground cover types (information classes). Here the whole image segment of interest is typically classified. Whereas training in Step 2 may have required the user to identify perhaps 1% of the image pixels by other means, the computer will label the rest by classification.

  5. Produce tabular summaries or thematic (class) maps which summarize the results of the classification.

  6. Assess the accuracy of the final product using a labeled testing data set.

24.3 Effects of LU/LC on Watershed Hydrology

Land cover plays a key role in controlling the hydrologic response of watersheds in a number of important ways. Changes in land cover can lead to significant changes in leaf area index, evapotranspiration, soil moisture content and infiltration capacity, surface and subsurface flow regimes including base flow contributions to streams and recharge, surface roughness, runoff, as well as soil erosion through complex interactions among vegetation, soils, geology, terrain and climate processes. Furthermore, land use modifications can also affect flood frequency and magnitude. Physiography and land cover determine the hydrologic response of watersheds to climatic events. However, vast differences in climate regimes and variation of landscape attributes among watersheds (including size) have prevented the establishment of general relationships between land cover and runoff patterns across broad scales.

24.3.1 Effect on Runoff/Stream Flow

1. Urban Watersheds

Urban watersheds are dominated by buildings, roads, streets, pavements, and parking lots. These features reduce the infiltrating land area and increase imperviousness. Because drainage systems are artificially built, the natural pattern of water flow is substantially altered. For a given rainfall event, interception and depression storage can be significant but infiltration is considerably reduced As a result, there is pronounced increase in runoff. Thus, an urban watershed is more vulnerable to flooding if the drainage system is inadequate. Once a watershed is urbanized, its land use is almost fixed and its hydrologic behavior changes due to changes in precipitation.

2. Agricultural Watersheds

An agricultural watershed experiences perhaps the most dynamically significant land-use change. Changing land use and the treatment usually lead to increased infiltration, increased erosion, and/or decreased runoff. Depression storage also is increased by agricultural operations. When the fields are barren, falling raindrops tend to compact the soil and infiltration is reduced. There is lesser development of streams in agricultural watersheds because small channels formed by erosion and runoff are obliterated by tillage operations.

3. Forest Watersheds

Interception is significant, and evapotranspiration is a dominant component of the hydrologic cycle. The ground is usually littered with leaves, stems, branches, wood, etc. The subsurface flow becomes dominant and there are times when there is little to no surface runoff. There is greater recharge of groundwater. Because forests resist flow of water, the peak discharge is reduced, although inundation of the ground may be increased. Complete deforestation could increase annual water yield by 20 to 40 %.

4. Mountainous Watersheds                              

The landscape of these watersheds is predominantly mountainous. Because of higher altitudes, such watersheds receive considerable snowfall. And such watersheds have substantial vegetation and thus interception is significant. Due to steep gradient and relatively less porous soil, infiltration is less and surface runoff is dominantly high for a given rainfall event. Flash floods are a common occurrence. The areas, downstream of the mountains, are vulnerable to flooding. Due to snow melt, water yield is significant even during spring and summer.

5. Desert Watersheds

There is little to virtually no vegetation in desert watersheds. The soil is mostly sandy and little annual rainfall occurs. Sand dunes and sand mounds are formed by blowing winds. Stream development is minimal. Whenever there is little rainfall, most of it is absorbed by the porous soil, some of it evaporates, and the remaining runs off only to be soaked in during its journey. There is limited opportunity for ground water recharge due to limited rainfall.

6. Coastal Watersheds

The watersheds in coastal areas may partly be urban and are in dynamic contact with the sea. Their hydrology is considerably influenced by backwater from wave and tidal action. Usually, these watersheds receive high rainfall, mostly of cyclonic type, do not have channel control in flow, and are vulnerable to severe local flooding. The water table is high, and salt water intrusion threatens the health of coastal aquifers, which usually are a source of fresh water supply. The land gradient is small, drainage is slow, and the soil along the coast has a considerable sand component.

7. Marsh or Wetland Watersheds

Such lands are almost flat and are comprised of swamps, marshes, water courses, etc. They have rich wildlife and plenty of vegetation. Evaporation is dominant, for water is no limiting factor to satisfy evaporative demand. Rainfall is normally high and infiltration is minimal. Most of the rainfall becomes runoff. Erosion is also minimal, except along the coast. The flood hydrograph peaks progress gradually and lasts for a long time.

24.3.2 Effect on Sediment Generation and Transport

Sedimentation can adversely affect reservoirs, waterways, irrigation systems and coastal zones. A change in the sedimentation load of a river can also affect the river’s biology and have implications in terms of fish production or biodiversity. Factors controlling sediment generation and export from a watershed include geologic structure, soil properties, topography, vegetation, land use, temporal and spatial distribution of precipitation and streamflow generation mechanisms. It is however, difficult to combine these factors into one reliable expression for estimating sediment discharge from a watershed or to isolate the individual effects of these factors. It is generally admitted that the bulk of sediment load of rivers originates from specific locations within the watershed, and that most of the sediments are brought into the river during extreme climatic events. There is clear evidence that changes in land-use practices can have a significant impact on the rate of erosion. Changes in land cover, from forest to agriculture for instance, usually induce an increase in soil erosion. On the other hand, good agricultural practices can substantially reduce the erosion hazard.

Keywords: Land Use Land Cover, Watershed Hydrology, Remote Sensing, Image Classification.

Suggested Reading

  • Giri, C. P. (2012). Remote Sensing of Land Use and Land Cover: Principles and Applications (Vol. 8). CRC Press.

  • Campbell, J. B. (2002). Introduction to Remote Sensing. Guilford Press.

  • Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote Sensing and Image Interpretation (No. Ed. 5). John Wiley & Sons Ltd.

  • Jensen, J. R. (2009). Remote Sensing of the Environment: An Earth Resource Perspective 2/e. Pearson Education India.

Last modified: Friday, 7 February 2014, 5:50 AM