Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

The semantic segmentation method based on high-resolution RGB images obtained by unmanned aerial vehicle (UAV) provides a cost-effective way to improve the accuracy of detection and classification in forestry. Few studies have explored the impact of sample distribution patterns on deep learning model detection accuracy. The study was carried out using the data from the 4.78 km2 RGB image of a chestnut (Castanea mollissima Blume) plantation obtained by the DJI Phantom 4-RTK, and the model training was conducted with 18,144 samples of manually delineated chestnut tree clusters. The performance of four semantic segmentation models (U-Net, DeepLab V3, PSPNet, and DeepLab V3+) paired with backbones (ResNet-34, ResNet-50) was evaluated. Then, the influence of chestnut data from different planting patterns on the accuracy and generalization performance of deep learning models was examined. The results showed that the combination of DeepLab V3 with ResNet-34 backbone gives the best performance (F1 score = 86.41%), while the combination of DeepLab V3+ with ResNet-50 backbone performed the worst. The influence of different backbone networks on the detection performance of semantic segmentation models did not show a clear pattern. Additionally, different spatial distribution patterns of chestnut planting affected the classification accuracy. The model MIX, trained on comprehensive training data, achieves higher classification accuracies (F1 score = 86.13%) compared to the model trained on single training data (F1 score (DP) = 82.46%; F1 score (SP) = 83.81%). The model performance in complex scenario data training is superior to that of the model in simple scene data training. In conclusion, comprehensive training databases can improve the generalization performance of chestnut classification with different spatial distribution patterns. This study provides an effective method for detecting chestnut cover area based on semantic segmentation, allowing for better quantitative evaluation of its resource utilization and further development of inventories for other tree species.

Section: Model Accuracy Evaluationmentioning

confidence: 99%

Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery

Sun,

Hao,

Guo

et al. 2023

“…Alternative image classification algorithms, such as decision tree [44,45] or neural network classifiers [46], have been widely used in aquatic vegetation mapping. For example, Zhao et al [47] and Luo et al [34,48] utilized Landsat TM and HJ-CCD imagery, respectively, to develop a decision tree algorithm for the detection of emergent, floating, and submerged aquatic vegetation in Taihu Lake.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Zhao et al [47] and Luo et al [34,48] utilized Landsat TM and HJ-CCD imagery, respectively, to develop a decision tree algorithm for the detection of emergent, floating, and submerged aquatic vegetation in Taihu Lake. The study of Rodriquez-Garlito [46] incorporates spectral indices from Sentinel-2 band combinations with a convolutional neural network classifier, in order to detect invasive aquatic plants. Villa et al [49] developed a rule-based classification scheme for mapping aquatic vegetation types, based on vegetation indices with consistent accuracy results.…”

Section: Introductionmentioning

confidence: 99%

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Tompoulidou,

Karadimou,

Apostolakis

et al. 2024

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

“…Previous remote sensing applications for mapping invasive species were typically based on a combination of spectral [26][27][28], texture, and object-based analysis [24,29,30] or functional features [31][32][33] of the target species extracted from optical multispectral [34,35], hyperspectral [36][37][38], and synthetic aperture radar (SAR) [39][40][41] data sources. For instance, studies have used both multispectral and hyperspectral imagery collected by airborne and satellite sources to detect woody and grass invasive species [6,42,43].…”

Section: Introductionmentioning

confidence: 99%

“…Studies using U-net CNNs to map woody vegetation extent with high spatial resolution satellite imagery in Australia reported an overall accuracy of approximately 90% [57]. Recently, CNNs have also been used in invasive species detection and have acquired promising results [28,[58][59][60][61][62][63][64]. For Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Xing,

An,

Guo

et al. 2024

Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.