Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Cai, Yaotong; Li, Xinyu; Zhang, Meng; Lin, Hui

doi:10.1016/j.jag.2020.102164

Cited by 61 publications

(29 citation statements)

References 55 publications

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“…The combined use of Sentinel-1 and Sentinel-2 data further improved the discrimination of wetlands and led to overall accuracies of 88%. The improved performance of combined Sentinel-1 and Sentinel-2 time series data was also confirmed by Cai et al (2020) [125], who found a 10% increase in overall accuracy of mapping different land covers in wetlands through the combined used of the data sets. The different land covers were found to have unique time series curves in both the optical and SAR domains, which led to improved discrimination of land cover types.…”

Section: Mapping Of Wetland Vegetationmentioning

confidence: 59%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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Section: Mapping Of Wetland Vegetationmentioning

confidence: 59%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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“…For example, machine learning models and similar techniques can be used to learn empirical relation between environmental drivers and patterns of exotic annual grass invasion (e.g., Williamson et al., 2020) and/or as a vehicle to solely address important prediction problems. Prediction problems may involve developing the most accurate maps of vegetation cover as possible, with less emphasis on understanding or explaining ecological processes shaping the underlying distributions, or best combining estimates from existing models (e.g., Bhatt et al., 2017; Cai et al., 2020; Healy et al., 2018). When emphasis is placed on explaining ecological process or conditions, researchers can leverage knowledge encoded within process‐based models and/or gained from experiments or observational studies to better guide or constrain machine learning activities for improving accuracies and understanding (Karpatne et al., 2017; Read et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning

et al. 2021

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“…Due to the inherently speckled nature of radar images, some researchers [66]- [68] have asserted that the segmentation of optical images is easier and more accurate. Their assertion can largely be backed by Figure 3, where most of the feature sets based on only S2 outperformed those based on only S1.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

et al. 2021

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The Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) considers agricultural fields as one of the essential variables that can be derived from satellite data. We evaluated the accuracy at which agricultural fields can be delineated from Sentinel-1 (S1) and Sentinel-2 (S2) images in different agricultural landscapes throughout the growing season. We used supervised segmentation based on the multiresolution segmentation (MRS) algorithm to first identify the optimal feature set from S1 and S2 images for field delineation. Based on this optimal feature set, we analyzed the segmentation accuracy of the fields delineated with increasing data availability between March and October of 2018. From the S1 feature sets, the combination of the two polarizations and two radar indices attained the best segmentation results. For S2, the best results were achieved using a combination of all bands (coastal aerosol, water vapor, and cirrus bands were excluded) and six spectral indices. Combining the radar and spectral indices further improved the results. Compared to the single-period dataset in March, using the dataset covering the whole season led to a significant increase in the segmentation accuracy. For very small fields (< 0.5 ha), the segmentation accuracy obtained was 27.02%, for small fields (0.5 -1.5 ha), the accuracy was 57.65%, for medium fields (1.5 ha -15 ha), the accuracy was 75.71%, and for large fields (> 15 ha), the accuracy stood at 68.31%. As a use case, the segmentation result was used to aggregate and improve a pixel-based crop type map in Lower Saxony, Germany.

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Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Cited by 61 publications

References 55 publications

Status of Phenological Research Using Sentinel-2 Data: A Review

Status of Phenological Research Using Sentinel-2 Data: A Review

Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning

Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

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