Developing Land Cover Classification System
for NOAA AVHRR Applications in Asia
Chandra Giri, Surendra Shrestha
UNEP Environment Assessment Programme for Asia and the Pacific
Asian Institute of Technology
P.O. Box 2754
Bangkok 10501
Thailand
Tel: (66-2) 524-6236
Fax: (66-2) 516-2125
Email: grid@cs.ait.ac.th
Abstract
In this paper, a land cover classification system for use with coarse spatial resolution remotely sensed data such as NOAA A VHRR, suitable for Asian conditions has been proposed. Considerations of Users' needs and sensor's capabilities in selecting land cover classes has been discussed. The need for harmonized and consolidated efforts to come up with better classification system, at different scale is noted.
1.0 Introduction
The classification system for remotely sensed data varies primarily with the kind of the satellite data used and the objective of the classification. Due to these variations, the nomenclature and definition of land cover types tend to vary considerably in the existing literature. As a result, today, comparison across time and space of land use has become very arduous ( Mucher et. al., 1993). Moreover, ambiguity in the mapping and classification criteria can often causes significant differences in the results of the interpretation (Singh, 1986). Efforts in developing standard and harmonized land cover types for Advanced Very High Resolution Radiometers (A VHRR) data interpretation is necessary as land cover cut across numerous sectors of resource management and the selection of classes plays an important role in the A VHRR analysis.
2.0 NOAA A VHRR Data
A VHRR data in 1 km resolution operated by the US National Oceanic and Atmospheric Administration are available either in LAC or HRPT .The data is suitable for macro assessment and monitoring of land cover status and their change patterns in near real time basis (Giri & Shrestha, 1995). Considering the strengths and weaknesses of the data (see Table 1), it can be said that this is the only option available for large scale
assessment and monitoring of resources, at least for few years until the moderate resolution satellite data will be available in the market .
Table 1.0 Advantages and Disadvantages of NOM A VHRR HRPT Data
| Advantages |
Disadvantages |
| 1. Synoptic coverage and hence low data Volume |
1. Coarse resolution (1.1 km at the Nadir) |
| 2. High radiometric resolution (10 bit) |
2. Pre-processing is time consuming |
| 3. Relatively low cost (Free!, only handling cost) |
3. The methodology in handeling AVHRR data for land applications is not well developed |
| 4. Twice daily coverage and hence high possibilities of having cloud free data. |
4. LAC data has limited capability to record on-board |
Negotiations are being held with China, India, Indonesia and Iran to extend the project in these countries. Two "hot spot" (major disturbance front) areas, one in Northern Laos and another in the Mekong Delta (Vietnam) have been identified for further investigation using high resolution satellite data such as LANDSA T TM and SPOT. Methodological guidelines on the use of A VHRR data for the assessment and monitoring of major land cover types in the region is being developed. These activities are expected to continue in 1995 and beyond.
The major land cover classes of interest were selected owing to the capability of the sensor to detect features on the earth's surface and their practical significance in the real world. For the purpose information such as variation in the phenological characteristics of the vegetation through different season and cropping system in the region were taken into consideration. Furthermore, information and experiences from previous similar exercises, guidelines prepared by UNEP/FAO on land cover/land use and experts' views on the field were also used in selecting classes. The principal aim is to harmonize the land cover classification system for regional aggregation and comparison.
The following diagram presents an overview of the land cover classification system.
Figure 1 Land Cover Classification System
3.0 Classification Criteria
The underlying supposition in the analysis of remote sensing data is that each
feature on the earth's surface records its unique signature in the sensor thus providing
opportunities for discriminating between different objects on the earth's surface. Features
on the earth's surface in-distinguishable with the satellite data should not be taken as a
separate class. On the other hand, all the classes discernible by the sensor may not be of
practical use to the users. A positive synergism of two is necessary in selecting land cover
classes prior to the analysis of the satellite data. However, this will not overcome the
possibility of incorporating secondary information with the help of GIS.
