Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images.

Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.

Spatio-temporal monitoring of marsh vegetation phenology and its response to hydro-meteorological factors using CCDC algorithm with optical and SAR images: In case of Honghe National Nature Reserve, China.

Vegetation phenology is a sensitive indicator which can comprehensively reflect the response of wetland vegetation to external environment changes. However, the time-series monitoring wetland vegetation phenological changes and clarifying its response to hydrology and meteorology still face great challenges. To fill these research gaps, this paper proposed a novel time-series approach for monitoring phenological change of marsh vegetation in Honghe National Nature Reserve (HNNR), Northeast China, using continuous change detection and classification (CCDC) algorithm and Landsat and Sentinel-1 SAR images from 1985 to 2021. We evaluated the spatio-temporal response relationship of phenological characteristics to hydro-meteorological factors by combining CCDC algorithm with partial least squares regression (PLSR). Finally, this study further explored the intra-annual loss and restoration of marsh vegetation in response to hydro-meteorological factors using the transfer entropy (TE) and CCDC-MLSR model constructed by CCDC and Multiple Linear Stepwise Regression (MLSR) algorithms. We found that the bimodal trajectory of phenology reflects two growth processes of marsh vegetation in one year, and high-frequency and high-amplitude loss occurred in shallow-water and deep-water marsh vegetation from April to October, resulting in the loss area within the year was significantly greater than the recovery area. We confirmed that the CCDC algorithm could track the evolution trajectory of time-series phenology of marsh vegetation. We further revealed that precipitation, temperature and frequency of water-level changes are the main driving factors for the spatio-temporal phenological evolution of different marsh vegetation. This study verified the effect of alternative changes of hydrology and climate on loss and recovery of marsh vegetation in each growth stage. The results of this study provide a scientific basis for wetland protection, ecological restoration, and sustainable development.