Mapping Tea Plantations from Multi-seasonal Landsat-8 OLI Imageries Using a Random Forest Classifier

Wang, Bin; Li, Jing; Jin, Xin; He, Xiaodong

doi:10.1007/s12524-019-01014-5

Cited by 18 publications

(13 citation statements)

References 49 publications

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“…Land coverage: To obtain coverage types, Landsat 8 satellite images were used for the years of study. The Python programming language was used for applying the Random Forest (RF) supervised classification method (Wang, Li, Jin, & Xiao, 2019). A total of 3000 regression trees were used to obtain the classification map for land usage and coverage.…”

Section: = * 100mentioning

confidence: 99%

Methodology for Estimating Landslides Susceptibility Using Artificial Neural Networks

Muñoz

Sánchez

Hernández

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this study, the susceptibility to landslides at Sevilla township, Valle del Cauca, located at southwest of Colombia was evaluated. The conditioning factors that involve the generation of landslides were evaluated using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. For the estimating susceptibility, an Artificial Neural Network (ANN) was implemented by applying the “Backpropagation” method to extract the synoptic weights of the conditioning variables (slopes, flow length, curvature, geology, fracture density, and land cover) on an automatic way with a data training module. The data for the analysis of the conditioning factors were carried out through a Digital Elevation Model (DEM) obtained through Radar Interferometry techniques, with Sentinel-1B satellite images for the year 2018. The results showed that Sevilla’s township has areas with high susceptibility, high slopes, and that it’s crossed by an active geological fault which implies that the earth's dynamics will condition the terrain stability.

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Section: = * 100mentioning

confidence: 99%

Methodology for Estimating Landslides Susceptibility Using Artificial Neural Networks

Muñoz

Sánchez

Hernández

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…can be used for tea plantation monitoring depending on the scale and aim of the study. For example, Wang [3] mapped tea plantations from multiseasonal Landsat-8 images using a random forest classifier. Snapir et al [4] monitored tea shoot growth using X-Band Synthetic Aperture Radar (SAR) images.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Unmanned Aerial Vehicle (UAV) Remote Sensing for Mapping Plucking Area of Tea Plantations

Zhang

Wan

Cao

et al. 2021

Forests

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Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations.

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“…It plays a major role in mapping and temporal dynamics of the extent of tea because of its ability for repetitive synoptic coverage and multispectral capability. In recent years, several space-borne sensors are available with the higher spatial and spectral resolution for identifying, mapping, and analyzing tea plantations at a regional scale (Prokop 2018;Wang et al 2019). Medium and low-resolution satellite images have been also deployed for mapping the spatial pattern and monitoring tea plantations (e.g., Landsat, IRS, SPOT, and Sentinel-2).…”

Section: Introductionmentioning

confidence: 99%

“…Medium and low-resolution satellite images have been also deployed for mapping the spatial pattern and monitoring tea plantations (e.g., Landsat, IRS, SPOT, and Sentinel-2). For instance, the Landsat-8 based Operational Land Imager (OLI) sensor data are used for detecting and analyzing tea plantations in the Anji County which is known for the major tea-producing regions in China (Wang et al 2019). Similarly, Sentinel-2 was used for mapping the spatial distribution of tea plantations in Northern Zhejiang in China (Li et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Based on spectral analysis, extracting tea plantations from fragmented landscapes can create difficulty. However, the terrain variability due to elevation and slope would lead to shading over tea plantations that increase the spectral variability of tea plants (Wang et al 2019). Despite the tea plantation area is increasing rapidly in India because of the economic and social prominence, no studies have investigated to assess the dynamics of the spatial pattern of tea and its health conditions.…”

Section: Introductionmentioning

confidence: 99%

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Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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Tea is an important cash crop, and it becomes necessary to map the spatial distribution of tea plantations. The tea industry has been expanding rapidly in the Northeast region of India, and consequently, the area under tea plantations is changing rapidly which needs periodic monitoring. In this study, satellite data such as Landsat-5 Thematic Mapper (TM) and Sentinel-2A were deployed for tea plantation identification and to analyze the dynamics of tea extent in the North Bengal district during 2000 and 2020. The result showed that the tea plantation area has increased from 1097 km 2 (2000) to 1377 km 2 (2020), depicting an increase in tea plantation area by 280 km 2 (or 25%) during the last 20 years. The highest increase in tea plantations was in Darjeeling (+137 km 2 ) and Jalpaiguri (+ 83 km 2 ) districts due to the expansion of tea estates along with the increasing population. The accuracy of tea plantation was evaluated using recall and precision with F1 score of 0.90 in 2000 and 0.92 in 2020. The tea plantation health condition was also assessed using both biophysical (LAI, EVI, FPAR) and biochemical parameters (leaf chlorophyll content). The results revealed that tea plants leaf area index (LAI) varied from 1 to 3, with healthy tea plants showed LAI > 2. Similarly, healthy tea plants depicted leaf chlorophyll content of 40 to 70 μg/cm 2 . Assessment of dynamics of tea plantation is required for promoting sustainable tea practices and safeguarding economic and social importance of people living in North Bengal.

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Mapping Tea Plantations from Multi-seasonal Landsat-8 OLI Imageries Using a Random Forest Classifier

Cited by 18 publications

References 49 publications

Methodology for Estimating Landslides Susceptibility Using Artificial Neural Networks

Methodology for Estimating Landslides Susceptibility Using Artificial Neural Networks

Exploring the Potential of Unmanned Aerial Vehicle (UAV) Remote Sensing for Mapping Plucking Area of Tea Plantations

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

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