Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Satellite Images
2.3. Land Cover Classification
- (1).
- The Golf area was modified by comparing vg67 and the satellite image of the target year.
- (2).
- The GIS area that was used to extract the Building area of 2015 was from vg67. The Building classification result for 2015 was used as the GIS area for 1996. Further, the Building classification result for 1996 was used as the GIS area for 1981.
- (3).
- The GIS area that was used to mask the Paddy field area was the paddy and upland areas of vg67 for 2015 and 1991 and vg3 for 1981. This is because the vegetation map may actually indicate paddy fields, even if they are upland fields. In addition, the Damaged paddy field area was added as Paddy field area for 2015 to ensure consistency with the Damaged paddy field area for 2016.
- (4).
- Bare land was extracted only by NDVI without the GIS area.
- (5).
- The GIS area that was used to mask Upland field was the paddy and upland areas of vg67 for 2015 and 1991 and vg3 for 1981. The reason for using the GIS area of paddy and upland areas as the mask is to maintain consistency with the Paddy field extracted in step 3.
- (6).
- The NDVI of burned grasslands in Season II has a lower value than that of other land covers [26]. However, the NDVI of unburned grasslands is similar to that of hardwoods, so they are indistinguishable. Therefore, the grasslands of vg67 were applied to Grassland for 2015. Since some parts of grasslands in vg67 were confirmed to have changed to conifers through plantation, these areas were adjusted to Conifer by the NDVI threshold value. Similar to the step for Building (step 2), the classification result for Grassland for 2015 was used as the GIS area for 1996. Furthermore, the classification result for Grassland for 1996 was used as the GIS area for 1981.
- (7).
- Conifer was extracted using two threshold values. However, in order to modify the results around the central cone, areas of Rhododendron kiusianum were excluded. For 2015 and 1991, vg67 was used for Rhododendron kiusianum areas, and vg3 was applied for 1981. The threshold value of 0.2546 was modified to 0.3265 for 1981 in order to improve the validity of the classification result, as mentioned later.
- (8).
- The remaining areas that were not classified into any land covers in steps 1–8 were Hardwood.
- (9).
- The River area created by the above method was overlaid.
- (10).
- The 2016 land cover map was generated by updating the 2015 land cover map with the Damaged paddy field and Bare land resulting from the landslide of the 2016 Kumamoto earthquake. For Bare land induced by landslides, the regions in which the difference between the NDVI of Season III in 2016 and that of Season III in 2015 was less than −0.2 were first extracted [17,18]. If the extracted area is actually bare land, then the vegetation cannot be recovered easily. Therefore, with the above method, the extracted area that represents the NDVI change shown in Figure 2 was identified as Bare land due to landslides.
- (11).
- The future prediction assumed that the damaged agriculture had recovered, and Damaged paddy field for 2016 was updated to Paddy field. Then, it was assumed that the unburned grasslands had changed to hardwoods. The unburned grasslands were updated to hardwoods by using the characteristic NDVI of unburned grasslands, as indicated by Yasunaka et al. [26].
- (12).
- The future Grassland area was determined as described in step 6. Thus, considering the transition from grasslands to hardwoods, the Grassland in the past should be naturally larger than that in the future. Therefore, we added a procedure to modify Hardwood to Grassland according to the 1996 and 1981 land cover maps. The relationship between NDVI changes from Season II to Season III for burned grasslands and hardwoods is ‘burned grasslands > hardwoods’. On the other hand, the relationship between NDVIs for each land cover in Season II, just after open burning, is revealed to be ‘burned grasslands < hardwoods’. Step 12 applied these relationships.
2.4. Validity Assessment of Land Cover Map
2.5. Estimation of Potential Groundwater Recharge
3. Results and Discussions
3.1. Validity of Land Cover Map
3.2. Land Cover Change
3.3. Potential Groundwater Recharge
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Year | Season I | Season II | Season III | Season IV | Season V |
---|---|---|---|---|---|
2017 | 19 February * | ||||
2016 | 20 March * | 23 May * | 11 August * | 30 October * | |
2015 | 21 May * | ||||
1997 | 1 April ** | ||||
1996 | 1 June ** | 5 September ** | |||
1982 | 31 July *** | ||||
1981 | 23 March **** | 3 June **** |
Band | Landsat-8 OLI | Landsat-5 TM | Landsat-2, 3 MSS |
---|---|---|---|
Red band (R) | band-4 | band-3 | band-5 |
Near-infrared band (NIR) | band-5 | band-4 | band-6 and band-7 |
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Amano, H.; Iwasaki, Y. Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan. Int. J. Environ. Res. Public Health 2020, 17, 6605. https://doi.org/10.3390/ijerph17186605
Amano H, Iwasaki Y. Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan. International Journal of Environmental Research and Public Health. 2020; 17(18):6605. https://doi.org/10.3390/ijerph17186605
Chicago/Turabian StyleAmano, Hiroki, and Yoichiro Iwasaki. 2020. "Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan" International Journal of Environmental Research and Public Health 17, no. 18: 6605. https://doi.org/10.3390/ijerph17186605