Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images

Fu, Bolin; Zuo, Pingping; Liu, Man; Lan, Guiwen; He, Hongchang; Lao, Zhinan; Zhang, Ya; Fan, Donglin; Gao, Ertao

doi:10.1016/j.ecolind.2022.108989

Cited by 33 publications

(15 citation statements)

References 59 publications

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“…It makes full use of the spectrum, shape, spatial relationship, and texture characteristics of ground objects, and to a certain extent reduces the Influence of same object with different spectrum and foreign object with same spectrum on classification, as well as the phenomenon of spectral interaction, so it can effectively avoid salt-and-pepper phenomenon [27]. Among current machine learning methods, the random forest (RF) has been an effective method widely used for wetland classification and mapping [49][50][51][52][53]. Thus, in this study, the object-based RF method was used for wetland classification based on remote sensing images listed in Table 1.…”

Section: Classification Methods and Accuracy Assessmentmentioning

confidence: 99%

Spatial and Temporal Dynamics of Wetlands in Guangdong-Hong Kong-Macao Greater Bay Area from 1976 to 2019

Liu

Cao

et al. 2022

Land

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Wetland ecosystems contain rich natural resources and vital ecological functions, and the investigation of spatial and temporal evolution characteristics of wetlands and their driving factors is critical for the management and conservation of wetlands. This study aimed to explore the spatial and temporal dynamics of wetlands in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 1976 to 2019 using multi-source remote sensing data (DISP KH-9, Landsat, and GaoFen-1), combing with the object-based classification method and landscape invasion index, and further analyze the driving forces affecting the spatial and temporal evolution of wetlands. The results showed that: (1) The total area of wetlands in the GBA showed a trend to first increase and then stabilize from 1976 to 2019. (2) The rapid development of aquaculture led to a continuous increase in aquaculture ponds and offshore aquaculture and a flat change in the middle and late stages, the area of mangroves declined substantially before 2000 and has gradually recovered since then, the invasion of various types of wetlands by built-up land is increasing, and wetlands are becoming increasingly fragmented. (3) The wetland changes in the GBA are the result of a combination of natural factors and human activities. Environmental conditions represent the basis for wetland dynamics, while the population, socio-economics, and policies are important drivers of wetland evolution. The findings will be beneficial to the understanding of wetland dynamic changes in the GBA over the past 40 years, and helpful to the scientific management and sustainable development of wetlands.

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Section: Classification Methods and Accuracy Assessmentmentioning

confidence: 99%

Spatial and Temporal Dynamics of Wetlands in Guangdong-Hong Kong-Macao Greater Bay Area from 1976 to 2019

Liu

Cao

et al. 2022

Land

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“…XGBoost belongs to the boosting family of ensemble learning. Compared to traditional GBDT algorithms, XGBoost uses a second-order Taylor expansion to approximate the generalization error of the objective function, simplifying the computation of the objective function [36]. It also introduces regularization terms to reduce model prediction variability and improve resilience against overfitting.…”

Section: Independent Variablesmentioning

confidence: 99%

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Zheng,

Zhang,

Deng

et al. 2024

Remote Sensing

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The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.

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“…Fu et al [13] efforts to ferocity UAV imageries with spaceborne Jilin-1 (JL101K) multispectral imageries to classify vegetation societies of karst wetland by means of the optimized Light Gradient Boosting (LightGBM), RF, and XGBoost procedures. This training likewise quantitatively assesses image fusion quality after spectral fidelity, three-dimensional and detail travels the properties of dissimilar image feature groupings and techniques on mapping vegetation societies by dimensionality reduction and flexible selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning

Firdaus

Kamil

2022

Int. j. commun. netw. inf. secur.

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Currently private and government agencies use remote sensing images (RSI) for various applications from military applications to agriculture growth. The images can be multispectral, panchromatic, ultra-spectral, or hyperspectral of terra bytes. RSI classification is considered one important application for remote sensing. Climate change detection especially affects numerous aspects of day-to-day lives, for instance, forestry management, weather forecasting, transportation, agriculture, road condition monitoring, and the detection of the natural atmosphere. Conversely, certain research works had a focus on classification of actual weather phenomenon images, generally depending on visual observations from humans. The conventional artificial visual difference between weather phenomena will take more time and error-prone. This paper develops a new reinforcement learning based climate change analysis on satellite multispectral image processing (RLCCA-SMSIP) technique. In order to properly determine climate change, the RLCCA-SMSIP technique employs residual network (ResNet-101) model for feature extraction. Next, deep reinforcement learning (DRL) approach is utilized for climate classification. Finally, parameter selection of the RLCCA-SMSIP technique involves sine cosine algorithm (SCA) for DRL model. For assuring the enhanced outcomes of the presented RLCCA-SMSIP model, comprehensive comparison results are assessed. The obtained values denote the supremacy of the RLCCA-SMSIP model on climate classification.

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Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images

Cited by 33 publications

References 59 publications

Spatial and Temporal Dynamics of Wetlands in Guangdong-Hong Kong-Macao Greater Bay Area from 1976 to 2019

Spatial and Temporal Dynamics of Wetlands in Guangdong-Hong Kong-Macao Greater Bay Area from 1976 to 2019

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning

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