The pruning phenological phase-based method for extracting tea plantations by field hyperspectral data and Landsat time series imagery

Xu, Weiheng; Huang, Shaodong; Wu, Chao; Xiong, Yujiu; Wang, Leiguang; Lu, Nanshu; Kou, Weili

doi:10.1080/10106049.2020.1801859

Cited by 9 publications

(4 citation statements)

References 38 publications

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“…Several studies have utilized high spatiotemporal-resolution multispectral imagery to evaluate the spatial distribution and area of tea plantations, including Li et al [83] , Huang et al [84] , and Dihkan et al [85] Various satellite resources, such as Sentinel-2 [3] and WorldView-2 [86] , have been used to examine the distribution and extraction of tea plantations. In addition, remote sensing technologies such as Landsat [87] , SAR [88] , Lidar [89] , and hyperspectral data [90] are also utilized for tea garden classification. When applying remote sensing technology to tea plantation extraction, color, texture, spectral, and terrain features are considered, including NDVI [86,91] , MNDVI [85] , EVI [88] , MNDWI [88] , LSWI [88] , GLCM texture [86] , Gabor texture [86] , DEM [88] .…”

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

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Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.

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Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

“…--Integrated imaging sensor options, rapid coverage, data output capability, unrestricted bank-angle, high data accuracy and integrity, and intensity capture with large dynamic range for exceptional lidar image quality [89,90]. …”

mentioning

confidence: 99%

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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“…In addition, the fact that the rubber plantation area increased by 33.53% in Menglun County, Xishuangbanna, from 1988 to 2006, while forest and cropland decreased by 21.16% and 12.68%, was discovered by Hu et al [5]. However, the LULC shifted from tropical forests of ecological importance and traditionally managed cropland to large rubber plantations and tea plantations, increasing the deterioration of the ecological environment in Xishuangbanna [6,7]. According to the research of some scholars, it is found that the large-scale change in LULC caused an imbalance in the internal structure and function of the natural ecological system [8].…”

Section: Introductionmentioning

confidence: 96%

Assessment of Spatial-Temporal Changes of Landscape Ecological Risk in Xishuangbanna, China from 1990 to 2019

Liu

Hong

et al. 2022

Sustainability

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Xishuangbanna is a major natural rubber and tea production base in China and a national nature reserve with the best-preserved tropical ecosystem. However, the extensive exploitation and use of land resources impact the land use/land cover (LULC) and the processes of regional landscape ecology, further causing a battery of ecological and environmental problems. It is necessary to evaluate landscape ecological risk objectively and quantitatively for improving the ecological environment and maintaining ecological balance. First, this study selected China Land Cover Dataset (CLCD) to analyze the changes in LULC. Second, we constructed the landscape ecological risk index (ERI) using LULC changes based on Google Earth Engine (GEE) platform. Third, the spatial-temporal pattern and spatial autocorrelation of landscape ecological risk were assessed in our study area. The results showed that the significant change in LULC was that the areas of cropland increased, and the areas of forests decreased during 1990–2019; the forests of a total area of 859.93 km2 were transferred to croplands. The landscape ecological risk kept a low and stable level from 1990 to 2019, more than 75% of the study area remained at the lower or lowest risk level, and in about 70% of the total study area, the ERI level maintained stability. In addition, the landscape ecological risk of the Xishuangbanna increased during 1990–2010 and decreased during 2010–2019. The ecological risk was a significant spatial autocorrelation and has been an aggregation trend in space from 1990 to 2019. Our research can identify key risk areas and provide a reference for the management and sustainable use of land resources, which promotes the understanding of landscape ecological risk and sustainable development of the ecological environment.

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“…The traditional methods for extracting tea plantations include field measurements and statistics, which are inefficient and not timely and do not meet the needs of modern agricultural development. To alleviate such problems, several scholars have conducted a series of studies on the extraction of tea plantations using multispectral remote sensing images, such as Sentinel [14][15][16], Modis [17], and Landsat [17][18][19] images. In addition, the methods they used are mainly traditional machine learning algorithms, such as decision tree (DN) [14,17,20], support vector machine (SVM) [15,16,21,22], maximum likelihood (ML) [23,24], and random forest (RF) [16,19,20,23].…”

Section: Introductionmentioning

confidence: 99%

Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks

et al. 2022

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Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions.

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The pruning phenological phase-based method for extracting tea plantations by field hyperspectral data and Landsat time series imagery

Cited by 9 publications

References 38 publications

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Assessment of Spatial-Temporal Changes of Landscape Ecological Risk in Xishuangbanna, China from 1990 to 2019

Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks

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