|
|
|
Poster Session 3
|
Determination of Land Use Change Categories Using Classification of Multitemporal Satellite Image Data
Result and Discussions
Accuracy assessment of three temporal data shows that most cover types were classified with acceptable level of accuracies (Table 1). The low classification accuracies found in urban class may be due to the spectral characteristics that are very similar to bare soil class. In three years, the forest land occupy about 40% of the study area and it has been continuously declined. The area of cropland and urban was proportionally increased. Since the data acquisition time of MSS and TM data used for this study was rather inappropriate for showing the green canopy, it might be difficult to correctly classify certain types of non-irrigated crops. Therefore, the bare soil class, which seems to be over estimated, may be a mixing class between pre-planted (or harvested) cropland and abandoned barren land. Although rice fields show the same seasonal pattern of leaf development, it could be easily classified by using the unique spectral characteristics from the water-covered group conditions and relatively dark soil.
year type | 1973 | 1984 | 1993 |
| Water | 0.919 | 1.000 | 1.000 |
| Forest | 0.996 | 0.991 | 1.000 |
| Urban | 0.458 | 0.566 | 0.693 |
| Rice | 0.890 | 0.937 | 0.983 |
| Crop | 0.520 | 0.904 | 0.700 |
| Soil | 0.993 | 0.970 | 0.998 |
| Overall | 0.831 | 0.906 | 0.901 |
Table 1. Classification accuracies of six cover types for three data sets.
Table 2 shows the definition and the areal extent of determined major change categories. Since we used three temporal data sets, there could be a few change categories that were used by three different cover types, such as forest in 1973, cropland in 1984, and urban in 1993. However, such categories account for very small portion of area and wee entitled into major change categories (i.e., forest-crop-soil is entitled as forest-soil category). Over the twenty years period, 18.4% (175,800ha) of the area have been undergone certain types of land cover changes. About 102,311 ha of forest lands have been converted to rather intensive land uses, such as agriculture and urban. Because of the persistent food shortage, it should be inevitable to find new lands to increase agricultural production. However, it was questionable whether the shifting of land uses was appropriate to sustain the environmental conditions of the land. Although large portion of bare baresoil area can be considered as non-irrigated crop land converted baresoil area may represent barren lands that were no longer capable to be used for food production.
| Land cover change category (1973-1993) | Area (ha) | % |
| Category | Form | To |
| A | Forest | Agriculture | 71,997.8 | 7.5 |
| B | bare soil | 14,136.1 | 1.5 |
| C | urban | 16,178.7 | 1.7 |
| D | Agriculture | bare soil | 27,877.1 | 2.9 |
| E | Urban | 31,489.9 | 3.3 |
| F | Water | rice field (polder) | 6,049.9 | 0.6 |
| G | Land | Water (reservoir, lake) | 4,864.1 | 0.5 |
| H | Miscellaneous | 3,173.3 | 0.3 |
| I | No-change | 779,439.0 | 81.6 |
| Total | 955,205.9 | 100 |
Table 2. Size of land cover change from 1973 to 1993.
Conclusions
Post-classification change detection is mainly used for the case where the classification of individual data set is almost correct. However, land cover classification may not be entirely correctly for the area where are great variation in vegetation phenology. In such cases, we have to cautious to determine the final change categories. Considering that classified cover type in each data set has inherent classification error, combining the classification results of more than two data sets should enlarge the mirespresentation of change characteristics . In this study, the change categories were determined by considering both classification accuracy and visual interpretation. Even though it was difficult to assess the final results of change analysis without field verified ground truth, we believed that the proposed method can provide more reliable information related to the land cover changes.
North Korea has shown a continuing effort to increase agricultural lands to solve the problem of food shortage during the last decades. The consequences and status of such land management, however, are not well clarified. Multitemporal satellite imageries were processed for the year of 1973, 1984, and 1993 to analyze the characteristics of land cover changes. Major land cover change categories identified was the conversion of forest of forest lands to agricultural uses, which comprises 58% of total changes occurred from 1973 to 1993. Overexploited forest lands should have great impacts to increase soil erosion and, consequently to degrade the capability of agricultural production of the land.
Reference
-
Eastman, J.R. and M. Fulk, 1993. Long sequence time series evaluation using standardized principal compoents, Photo. Enger. And Remote Sensing, 59(6):991-996.
-
Jensen, J.R., 1996, Introduction digital image processing, Prentice Hall.
-
Lambin, E.F. and D. Ehrlich, 1997. Land-cover changes in sub-Saharan Africa (1982-1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at continental scale. Remote Sensing of Environment, 61-:181-200.
-
Miller, A.B., E.S. Bryant, and R.W. Birnie, 1998. An analysis of land cover changes in the Northern. Forest of New-England using multitemporal Landsat MSS data, Int. J. Remote Sensing, 19(2):245-265.
-
Singh, A., 1989, Digital change detection techniques using remotely-sensed data, Int. J. Remote Sensing, 10(6):989-1003.
-
Washington-Allen, R.A., R.D. Ramsey, B.E. Norton, and N.E. West 1998. Change detection of the effect of severe drought on subsistence agropastoral communities on the Boivian Altiplano, J. Remote Sensing, 19(7):13190-1333.
|
|
|
|
|
|
|
