Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R2) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%

Optimization Of Freeze-Dried Starter For Yogurt By Full Factorial Experimental Design

With the rapidly development of fermented milk product, it is significant for enhancing the performance of starter culture. This paper not only investigated the influence of anti-freeze factors and freeze-drying protective agents on viable count, freeze-drying survival rate and yield of Lactobacillus bulgaricus (LB) and Streptococcus thermophilus (ST), but also optimized the bacteria proportion of freeze-dried starter culture for yogurt by full factorial experimental design. The results showed as following: the freeze-drying protective agents or anti-freeze factors could enhanced survival rate of LB and ST; the freeze-dried LB and ST powders containing both of anti-freeze factors and freeze-drying protective agents had higher viable count and freeze-drying survival rate that were 84.7% and 79.7% respectively; In terms of fermentation performance, the best group of freeze-dried starter for yogurt was the compound of LB3 and ST2

Achievements of schistosomiasis control in China

The control of schistosomiasis has been spectacularly successful in terms of controlling endemicity and severity of the disease during the last 50 years. It can be categorized into two stages. From 1955 through 1980, the transmission-control strategy had been widely and successfully carried out. By the end of 1980, the epidemic of schistosomiasis was successfully circumscribed in certain core regions including areas at the middle and low reaches of the Yangtze River and some mountainous areas in Sichuan and Yunnan provinces, where control of schistosomiasis had been demonstrated to be very difficult to be sustained. Therefore, since 1980, schistosomiasis control in China has been modified to employ a stepwise strategy, based on which morbidity control has been given priorities and if possible transmission control has been pursued. However, since snail-ridden areas remain unchanged so far, reinfections occur frequently. This necessitates a maintenance phase to consolidate the achievements in the control of schistosomiasis. In the mean time, we are challenged with some environmental, social and economical changes in terms of controlling schistosomiasis. Successfully controlling schistosomiasis in China is still a long-term task but will be achieved without doubt along with the economic development and the promotion of living and cultural standard of people

Response Surface Optimization of Lyoprotectant from Amino Acids and Salts for Bifidobacterium Bifidum During Vacuum Freeze-Drying

High quality probiotic powder can lay the foundation for the commercial production of functional dairy products. The freeze-drying method was used for the preservation of microorganisms, having a deleterious effect on the microorganisms viability. In order to reduce the damage to probiotics and to improve the survival rate of probiotics during freeze-drying, the Response Surface Methodology (RSM) was adopted in this research to optimize lyoprotectant composed of amino acids (glycine, arginine) and salts (NaHCO3 and ascorbic acid). Probiotic used was Bifidobacterium bifidum BB01. The regression model (p<0.05) was obtained by Box–Behnken experiment design, indicating this model can evaluate the freeze-drying survival rate of B. bifidum BB01 under different lyoprotectants. The results indicated these concentrations as optimal (in W/V): glycine 4.5%, arginine 5.5%, NaHCO3 0.8% and ascorbic acid 2.3%, respectively. Under these optimal conditions, the survival rate of lyophilized powder of B. bifidum BB01 was significantly increased by 80.9% compared to the control group (6.9±0.62%), the results were agreement with the model prediction value (88.7%)