Study area
Panjin (E 121°25′–122°31″, N 40°39′–41°27″) is located at southwest of Liaoning province in China, the north bank of Bohai Sea, and the center of Liao River delta and has a coastline of 118 km (Fig. 4). Panjin enjoys an average altitude of 4 m and gentle terrains, and the highest and lowest elevations are 18.2 and 0.3 m, respectively. It has in total 21 big or small rivers. The Shuangtai river mouth wetland nature reserve (E 121°28′10″–122°00′24″, N 40°45′00″–41°08′50″) has a total area of 4084 km2 and has been formed after the Shuangtaizi River flows to seas.
Data fusion of the study area
Through extraction and superposition, we analyzed the changes of NDVI and NDWI between two time phases in Panjin, as well as the changes of vegetation coverage index. Vegetation coverage measures the proportion of vertical projected areas of leaves, stems, and branches to the total area of the study site. Vegetation coverage well reflects the seasonal changes of vegetation growth, ecological environment, water/soil quality, and water conditions in wetlands and is a favorable indicator of seasonal changes in wetlands [18, 19].
The modified STARFM was used to fuse and predict the RS images at two phases of 2016 in Panjin (Fig. 5).
Clearly, based on the high-space-time resolution MODIS data determined from the space-time fusing algorithm, the RS data were effectively improved at both spatial and temporal scales. The image quality improvement from the perspective of data source can theoretically and efficiently improve the precision of information extraction.
Changes of NDVI and NDWI
NDVI and NDWI were extracted from the merged images of Panjin in June and August 2016, and the changes of NDVI and NDWI between the two time phases were analyzed through superposition. NDVI as a commonly used vegetation index can effectively reflect the vegetation information [20, 21]. Due to the vast areas and diverse types of water bodies (big and small rivers, reservoirs, ponds, shallow swamps) in the wetlands of Panjin, we introduced NDWI to reflect the distribution of water bodies.
Analysis of experimental results shows the NDVI of a part of pixels exceeds the range (− 1, 1), which is because of errors of atmospheric correction in the FLAASH of TM images as well as the trimmed boundaries. The abnormal data were removed. Specifically, values > 1 were assigned as “1,” and the values < 1 were treated as “− 1.” The abnormal data were calculated as − 1.0 > b1 < 1.0 by using the Band Math on ENVI (Fig. 6).
Clearly, in June and August, the majority of Panjin was covered by vegetation, and a part of the water bodies were even found with large NDVI, which indicate the large chlorophyll concentrations in the water bodies. Data superposition between two phases (data in August–data in June) can reveal the changes of NDVI and NDWI. Clearly, the NDVI throughout Panjin increases but NDWI decreases from June to August. Meteorological data show heavy rainfall (321 mm) occurred in June 2016 in Panjin, but the rainfall in August was smaller (160 mm). The experimental results are basically consistent with the real data.
Changes of vegetation coverage
Vegetation coverage measures the proportion of vertical projected areas (leaves, stems, branches) to the total area of the study site. Vegetation coverage well reflects the seasonal changes of vegetation growth, ecological environment, water/soil quality, and water conditions in wetlands and is a favorable indicator of seasonal changes in wetlands. The development of RS offers powerful technical support for estimation of large-area regional vegetation coverage. Many methods can be used to detect and calculate vegetation coverage with the help of RS. Here, an improved model based on pixel equinoctial model through experiments was used.
To extract more accurate vegetation coverages, we first used two phases of NDVI (set NDVI > 0.0), firstly extracted the vegetation information with the preset threshold, and then computed vegetation coverage. The vegetation coverages on 18 June and 16 August 2016 in Panjin are showed below. Clearly, the majority of Panjin was covered by vegetation; the overall vegetation coverage in June was smaller than in August, which was mainly because vegetation grew further under sufficient rainfall, and rice, reed, and other crops grew well.
The vegetation coverage rates in Panjin and its districts and counties were summarized on ArcGIS. The average vegetation coverage rate in the whole urban areas of Panjin on 18 June 2016 was 0.71 and rose by 0.02 to 0.73 in 16 August. Statistics by districts and counties showed the vegetation coverage rates in June were all smaller than in August and were smaller in urban areas than in other areas.
To facilitate statistics and analysis, we further divided vegetation coverage into six levels: bare areas (0, 0.1), low coverage [0.1, 0.3), medium to low coverage [0.3, 0.45), medium coverage [0.45, 0.6), and high coverage [0.6, 1). Then, the divisions were segmented by colors and densities to classify the vegetation coverage rates (Fig. 7). Clearly, the whole city in 2016 was dominated by high to medium coverage. The vegetation coverage rate in the wetlands was higher in August than in June. The land covers with high coverage (> 0.6) were reeds and rice lands, and the land covers with low coverages included beaches and sea shores (0.1–0.3). The vegetation coverage of wetland vegetation especially reeds rose rapidly.
Previous studies and this study suggest wetland resources are largely influenced by climate, and especially, the changeable meteorological conditions in Panjin between June and September with cloudy and rainy climate make the corresponding Landsat images unavailable. The RS space-time fusing technique provides an effective way to solve the seasonal changes of wetlands, to modestly overcome the limitations of traditional investigation methods, and solve the technical problems in applications of wetland resource investigation.