Effect of Landuse Derivation Strategies
from Remote Sensing Data on Hydrologic Modeling
Srikantha Herath1 and Dushmanta Dutta2
International Center for Disaster-mitigation Engineering
Institute of Industrial Science
University of Tokyo
7-22-1, Roppongi, Monato-ku, Tokyo 106, Japan
1Tel: 03-3402-6231(ext.2661) Fax: 033402-4165
Email : hearth@incede.iis.u-toktyo.ac.jp
2Tel:03-3402-6231(ext. 2663) Fax: 03-3402-4165
Email : dutta@incede.iis.u-tokyo.ac.jp
Abstract
Distributed hydrologic models can be used to simulate hydorlogic processes in a catchment considering the spatial variability of catchment characteristics such as landuse, topography and other physical parameters. Remote sensing images provide a very effective means of estimating landuse parameters of a catchment as they can be used to estimate the temporal and spatial variation of a large area at high resolution. In this study the landuse of the Agno river basin in the Philippines was derived from SPOT satellite images using different landuse classification strategies, which include supervised, unsupervised and hierachical classifications. The best classification images ere judged by aerial photographs and site visits. The result show that there is a marked difference in the landuse categories that could be delineated from satelite image. Hydrological simulations were carried out with a distributed hydrologic model using different landuse maps obtained to identify the model sensitivity to the landuse maps. Results show different components of the hydrological cycle are affected to different degree depending on landuse information
1. Introduction
Hydrologic models are used to estimate the catchment response to the rainfall input. Mainly they are used to estimate river flows as a result of rainfall input to the catchment. Traditionally, the hydrologic models consisted of lumped models which represent the catchment and compute the discharge at a point. As the model parameters cannot be related to measurable catchment characteristics they require a long period of observations, both river flow and the rainfall, to calibrate the model parameters so that the model computations match the observed river discharges. However, in recent years, with increasing computational power at hand, it is becoming possible to use physically based catchment models to describe the hydrologic processes in the catchment. These models employ the governing equations of the water flow in its various phases, and simulate its movement through rainfall, evaporation, infiltration, subsurface flow, groundwater flow and river flow. Since there are no analytical solutions to any of the governing equations, coupled numerical models are used for the solution, which make it necessary to discretize the catchment into a number of grids, in the horizontal and vertical planes. Also due to the nature of the governing equation,s it is necessary to assume these ujits to be homogeneous in relation to some hydrologic properties, such as soil hydraulic characteristics. Therefore in order to set up a distributed hydrologic model, a large amount of distributed input should be prepared. Once such models are prepared they can be used to estimate not only the river flows at any point along the river, but also to estimate spatial distribution of other hydrologic variables such evapotranspiration, groundwater heads and soil moisture distribution. One important area of application of distributed models is the assessment of landuse change on the hydrologic regime.
Remote sensing data are an important source to estimate landuse paramenters effectively over large area at high resolution. They are attractive as means of obtaining temporal variation of landuse and to study its effect on hydrologic parameters. Therefore, it is important to identify the limitations of using satellite data in terms of sensitivity to the classification, especially such techniques are to be used in forecasting effects of landuse changes to the hydrologic techniques are to be used in forecasting effects of landuse changes to the hydrologic environment. In this paper, first we use SPOT image to derive a landuse map for a catchment under investigation, and then using the resulting landuse maps, we carry out hydrologic analysis to asses the sensitivity of these classification schemes.
2. Study Area
A sub catchment of the Agno river basin, the thrid larges river in the Philippines which lines in the western portion in the central part of Luzon island from 120o00' to 121o00'east longitude and from 15o00'to 16o00' north latitude with a drainage area 5.952 sq. km, is selected as the study are (National Water Resources Council, 1983). The sub catchment encompansses an area of 1,7000 sq. km and it is an ideal site for this study as both satellite images and data required for hydrological model simulation are available for the part of the basin (Fig.1). The annual average rainfall varies from 2,000 mm in the south-eastern part of the study area to more than 4,000 mm in the northern mountainous area.
Figure 1 Agno River Basin, the Philippines
The study area can be distinctly divided into two parts based on the variation of landuse pattern i.e. the upper mountainous area and lower flat area. The includes dense and light forest areas, whereas, lower flat area is convered by agricultural fields and grass lands. In the lower part, along the Agno river, residential areas are visible.
3. Landuse Classification
3.1 Image selection
The SPOT HRV digital images are used for this study for landuse classification. The digital data was acquired for the study area for January 10, 1993, processed at the 1B level by SPOT Image Corp. This level of processing includes radiometric correction of variations in data that are caused by scanner malfunction and atmospheric interference. The imagery was acquired under excelled surface conditions. No precipitation had fallen during the preceding 20 days, and cloud cover on the image was negligible.
3.2. Geometric Correction
Geometric correction addresses problems associated with skew, rotation, and perspective in raw remotely sensed data (ERDAS, 1990). After importing the dataset to the proper digital format, was overlayed with the catchment boundary and river network data, digitized from 1:50,000 scale maps, to verify the geometric correctness of the dataset. Transformation and rectification of the image were done using the digitized river network for geometric correction of the dataset.
3.2 Classification
Supervised classification using the original bands and their subsequent transformations into NDVI (Normalized Difference Vegetation Index) and Soil Brightness Index (SBI) were performed to determine accuracy of identification of different landuse classes utilizing the satellite date (Table 1). The maximum likelihood and minimum distance classification algorithms were used in the classification.
Table 1: Image Data Transformations
| Transformed Indices |
| NDVI = (Infrared -Red) / (Infrared + Red) |
| SBI = (0.332 X Green) + (0.603 x Red) + 0.262 x Infrared) |
| (where, Green = band 1, Red =band2, and Infrared = band 3) |
Training area for supervised classification were taken from the aerial photographs available for the lower part of the study area, whereas for upper part of the basin, photographs taken during field visits were used to define the training areas. Training areas were selected uniformly for these classes. Supervised classification was performed using the selected training areas initially, for original three bands. However, the outcome was not satisfactory compared to the available ground truth data, and hence, further supervised classifications were conducted by changing the ratios of training areas and using additional bands i.e. NDVI and SBI. The resulting maps such as landuse compared with the aerial photographs as well as with the existing landuse maps such as landuse maps prepared by Swedish Space Corporation (SSC) in 1988 (Rasch, 1991). However, the outcomes from further supervised classifications were also not satisfactory for some classes to expected degree of accuracy. Hence, a more comprehensive approach was taken to classify the image using hierarchical statistical processing procedures (Baumgartner and Apel, 1996).
3.3 Hierarchical Statistical Processing Procedures
These procedures represent a more refined approach for classifying remote sensing data. Instead of processing all spectral bands with a large number of categories in one step, the number of spectral bands and categories is adapted to specific problems. Density slicing of each band was carried out to identify the range of reflectance values representing different classes and non-representative parameters were classified further using either supervised or by unsupervised learning techniques after extracting the representative parameters. Depending on the problem, several classifications - each resulting in a bit mask (thematic maps) - are carried out. After this multi-step classification, all the resulting bit masks are combined into final classification map.
