Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Wetlands play irreplaceable roles in climate regulation, water purification, biodiversity conservation, and natural ecology balancing [1]. Wetlands provide habitats for wild animals and plants, and guarantee climate modulation, pollutant deterioration and decomposition, flood detention, water conservation, soil erosion control, oxygen release from solid carbon, and ecological nutrient cycling [2]. Wetlands offer humans abundant animal and plant resources and necessary water sources, and provide turf and other special sources [3]. However, due to natural and artificial interferences, numerous wetlands have been converted to farm lands or urban lands, and such fundamental variation of wetland properties has roused wide attention from researchers [4].

Remote sensing (RS) has been extensively used to investigate and identify wetland resources. RS brings about huge technical support to relevant research owing to its time/cost saving, provision of abundant information, multi-platform operation, and multi-temporal advantage [5]. Though RS largely facilitates wetland research, the changeable meteorological conditions make cloud interference one of the major barriers against the acquisition of clear RS images, identification of wetlands, and information extraction. Especially, the cloudy conditions at the growing season (June to September) certainly complicate the extraction of wetland-related information (in particular, marsh wetlands). Though there are some cloud processing techniques, they are limited by image distortion and information loss [6]. In the field of wetland monitoring, the most used and mature RS source is Landsat Thematic Mapper (TM). Despite its spatial resolution up to 30 m, its visiting period is only 16 days, and the occurrence of cloudy or raining weather would largely reduce the usability of TM data [7]. Especially for wetlands, the variational climates and cloudy or rainy weathers occur more frequently at the high-flow period, when the RS data become less available. Nevertheless, this limitation can be supplemented by the National Aeronautics and Space Administration (NASA) Terra moderate-resolution imaging spectroradiometer (MODIS) sensor, which provides data anywhere nearly every day and is complementary with Landsat Thematic Mapper (TM). Thus, MODIS data largely improves the possibility of acquiring rain- and cloud-free RS images on the polar-orbiting platform [8]. However, the spatial resolutions of MODIS are very low (only 250–1000 m), which are unsatisfactory for wetland resource monitoring at the medium or high scale [9].

If the high-spatial-resolution data of Landsat TM and the high-temporal-resolution data of MODIS can be effectively fused, we could obtain a time series of Landsat TM images with high space-time resolutions simultaneously, which would largely improve the reasonableness and precision in regional dynamic monitoring of wetland resources. In recent years, the space-time fusing RS techniques and different RS sources have been developed rapidly. Researchers have constantly studied effective RS data fusing techniques, aiming to acquire high-space-time resolution RS data. Gao et al. [10] studied the space-time fusion of Landsat and MODIS data and thereby established a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), effectiveness of which was validated by relevant tests. Later, the defects and limitations of STARFM were modified. Zhang et al. [11] established an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Wu et al. [12] used a hybrid pixel decomposition method, Spatial and Temporal Data Fusion Model (STDFM), to efficiently fuse MODIS and TM images. Based on STARFM, some scholars built an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM), which was contributive to improving fusion algorithms [13]. Xie et al. [14] used the Combination of Downscaling Mixed Pixel Algorithm and Spatial and Temporal Adaptive Reflectance Fusion Model (CDSTRFMA) into RS data fusion. It was found CDSTARFM outperformed both STARFM and the downscaling mixed pixel algorithm with a 2% higher precision. Walker et al. [15] used STARFM to fuse Landsat and MODIS data and effectively characterized the typical phenology of forest vegetation in arid regions. Jia et al. [16] studied the classification mapping of land covers in Beijing, or namely fused Landsat Normalized Difference Vegetation Index (NDVI) time series using STARFM.

In all, STARFM has been preliminarily applied and experimentally improved since its emergence. However, the concrete applications of STARFM still require the algorithmic improvement according to the characteristics of the study areas. In this study, the study area was located in the east of Liaoning province, China. We utilized the complementary advantages between MODIS and Landsat images in time and spatial resolutions, and adopted a progressive band algorithm. Through the comparison of fusion precision, the algorithm feasible for data fusion in the study area was screened out and its precision was evaluated. The seasonal changes of NDVI, Normalized Difference Water Index (NDWI), and vegetation richness index in wetlands of Panjin city were studied, which theoretically underlie the research on dynamic changes of wetland resources in scientific rational zones.

The RS space-time fusing technology makes full use of the advantages of different information sources. The improved STARFM generates high-precision multi-temporal RS data, which provide a new valid RS source for land resource RS-based investigation. It is experimentally validated that MODIS and Landsat images can be well fused and the fused images are highly correlated with the TM images. In particular, the correlations rank by NDWI (0.9678) > green band (0.9630) > near-infrared band (0.9345) > blue band (0.9018). This method can be referred to in traditional RS-based land resource investigations, including wetlands.

Due to influences by climate, terrains, and soils, the wetland resources are complex and changeable. Moreover, the majority of wetlands are geographically remote and traffically inconvenient, so the traditional monitoring methods are limited by the huge material costs, vast investigation extent, destruction to wetlands, long investigation period, and low time effectiveness. In this study, data fusion was used to study the seasonal changes of wetlands in Panjin. The two phases of RS images in June and August 2016 were inverted. It was found that the fused images well reflected the seasonal changes of NDVI, NDWI, and vegetation coverage in the wetlands and modestly solved the limitations in the cloudy and rainy climate.

The RS space-time fusing technology can be used to study the dynamic changes in the distribution, types, and areas of wetlands, to investigate the distribution, trends, and spatial patterns of wetland biological resources and to invert the daily or seasonal changes of near-ground atmospheric CO₂ concentrations, vegetation growing trend, hydrological characteristics, surface suspended matter concentrations, and spatial patterns in the wetlands.

In the high-water period (June to September) of wetlands, the presence of cloud and rain leads to massive data loss and useless Landsat images. The traditional RS methods for classifying and extracting regional wetlands would cause severe interferences. Thus, an effective way is to use STARFM to fuse MODIS and Landsat images, solving the problem of data loss, and thereby to extract and monitor the wetland-related information. However, when an RS space-time fusing method is used to extract information of wetlands, both the phenologic changes and space information of wetlands should be taken into account, especially in regions with severe space heterogeneity. Thus, one trend in the future is to converse scale or fuse other RS data and thereby to effectively solve the problem of space heterogeneity.

Influenced by factors such as climate, topography, and soil, wetland resources possess the characteristics of complex and changeable land types, and the geographical location of most wetlands is remote with poor transportation. Only relying on conventional means to monitor wetland situation not only requires a large amount of material cost with small investigation scope, but also causes damage to the wetland, which has a long period of wetland research and insufficient timeliness. The time-space fusion of remote sensing data effectively gives play to the advantages of different information sources of MODIS and Landsat data; the remote sensing information source obtained by modified STARFM model fusion algorithm has the advantages of multidate, high precision, etc., providing an effective remote sensing information source for remote sensing investigation of land resource. Experiment proves that MODIS and Landsat data can be fused in a good manner, the image after fusion has high correlation with TM image, and this method can provide practical reference for the remote sensing investigation of traditional land resources including wetland resource. Research results show that the image after fusion can well reflect the vegetation index, water body index, and vegetation coverage of the wetlands’ seasonal variation, which solve the research problem of wetland seasonal characteristics in cloudy and rainy weather to some extent.