Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Lin, Ping; Lu, Qun; Du, Li; Chen, Yongming; Zou, Zhiyong; Jiang, Shanchao

doi:10.1111/nrm.12248

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…Several firms are utilizing publicly accessible and exclusive high-resolution satellite images, along with other datasets, to generate intricate maps of forests and landcover [45,46] . As an illustration, SilviaTerra integrates publicly accessible, detailed satellite images with data collected by the US forest department through field surveys.…”

Section: Estimating Carbon Biomass and Inventorying Forestsmentioning

confidence: 99%

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Raihan

2023

Nat. Resour. Conserv. Res.

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The recent progress in data science, along with the transformation in digital and satellite technology, has enhanced the capacity for artificial intelligence (AI) applications in the forestry and wildlife domains. Nevertheless, the swift proliferation of developmental projects, agricultural, and urban areas pose a significant threat to biodiversity on a global scale. Hence, the integration of emerging technologies such as AI in the fields of forests and biodiversity might facilitate the efficient surveillance, administration, and preservation of biodiversity and forest resources. The objective of this paper is to present a comprehensive review of how AI and machine learning (ML) algorithms are utilized in the forestry sector and biodiversity conservation worldwide. Furthermore, this research examines the difficulties encountered while implementing AI technology in the fields of forestry and biodiversity. Enhancing the availability of extensive data pertaining to forests and biodiversity, along with the utilization of cloud computing and digital and satellite technology, can facilitate the wider acceptance and implementation of AI technology. The findings of this study would inspire forest officials, scientists, researchers, and conservationists to investigate the potential of AI technology for the purposes of forest management and biodiversity conservation.

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Section: Estimating Carbon Biomass and Inventorying Forestsmentioning

confidence: 99%

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Raihan

2023

Nat. Resour. Conserv. Res.

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“…With the evolution of modern technology, artificial intelligence (AI) empowers the comprehensive optimisation and upgrading of map mapping from digitisation to intelligentisation. Thus, the process of integrating terrain features on various data (i.e., satellite and UAV images) (Drăguţ & Eisank, 2012; Na et al, 2021) with AI algorithms (i.e., machine learning, deep learning and ensemble learning) (Lin et al, 2020; Zhao et al, 2020; Zhou, Zhou, et al, 2021) to intelligently identify and classify surface landforms is the so‐called intelligent landform classification (Sunaga et al, 2019). Currently, automatic landform classification has achieved good results by means of combining AI and DEM data, for instance, incorporating the data‐ and feature‐level fusion into the object detection (Wang & Li, 2021).…”

Section: Introductionmentioning

confidence: 99%

Intelligent classification and analysis of regional landforms based on automatic feature selection

Xu,

Zhu,

et al. 2023

Earth Surf Processes Landf

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Terrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend.

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“…However, the drawback of this research is getting data from satellites. This problem was reportedly resolved in [22], who is researching the vegetation of wetlands. This work contrasts SVM, CMER, and SCG-MLP with other classification methods.…”

Section: Introductionmentioning

confidence: 99%

Implementation of deep learning based semantic segmentation method to determine vegetation density

Sari

Arifin

Novitasari

et al. 2022

EEJET

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The dryness of peatlands is influenced by the density of vegetation. If peatlands are dry, they become vulnerable to a fire risk. To calculate the drought index, professionals must conduct a vegetation density analysis. However, field analysis requires vast amounts of resources. Moreover, the accuracy of the analysis based on satellite data is not adequate. Therefore, this research presents drone-captured two-dimensional image data. The object of this research is The Liang Anggang Protection Forest Block I in Banjarbaru, South Kalimantan, Indonesia. It is surveyed for information on its vegetation cover. Afterwards, There are 300 images of vegetation cover collected and utilized in total. The method of deep learning with semantic segmentation will be used to compare the results of determining methods with expert results as ground truth. The contribution of this study is to determine the optimal performance of deep learning model used for classifying vegetation density into three categories: bare/ungrazed, lightly grazed, and heavily grazed. Performance is evaluated based on correctness and intersection over union (IoU). Obtaining the proper parameters for the classification model using deep learning techniques and comparing the results of the best segmentation model are the objectives of the following contribution. From experimental studies conducted, the optimal momentum parameter value for MobileNetV2, Xception, and Inception-ResNet-v2 is 0.9, and the optimal accuracy performance is 82.69 percent on average. The most appropriate momentum for ResNet 18 architecture is 0.1. The result of semantic segmentation using the DeepLabV3 model with Inception-ResNet-v2 architecture is the optimal model for estimating vegetation density compared to U-Net model.

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Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Cited by 7 publications

References 48 publications

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Intelligent classification and analysis of regional landforms based on automatic feature selection

Implementation of deep learning based semantic segmentation method to determine vegetation density

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