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MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
Citation: MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

doi: 10.13203/j.whugis20200633
Funds:

The Fundamental Research Funds for the Central Universities (LGYB201704)

  • Received Date: 2020-11-19
    Available Online: 2021-05-07
  • Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
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Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

doi: 10.13203/j.whugis20200633
Funds:

The Fundamental Research Funds for the Central Universities (LGYB201704)

Abstract: Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.

MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
Citation: MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
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