Mapping the Spatial Distribution of Tea Plantations Using High-Spatiotemporal-Resolution Imagery in Northern Zhejiang, China
Open Access
- 30 September 2019
- Vol. 10 (10), 856
- https://doi.org/10.3390/f10100856
Abstract
Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial distribution and area of tea plantations. In this study, we developed a simple method to accurately map tea plantations based on their unique phenological characteristics observed from VENμS high-spatiotemporal-resolution multispectral imagery. The normalized difference vegetation index (NDVI) and red—green ratio index (RGRI) of time series were calculated using 40 VENμS images taken in 2018 to evaluate the phenology of tea plantations. The unique phenological period of tea plantations in northern Zhejiang is from April to May, with obvious deep pruning, which is very different from the phenological period of other vegetation. During this period, the RGRI values of tea plantations were much higher than those of other vegetation such as broadleaf forest and bamboo forest. Therefore, it is possible to identify tea plantations from the vegetation in images acquired during their phenological period. This method was applied to tea plantation mapping in northern Zhejiang. The NDVI value of the winter image was used to extract a vegetation coverage map, and spatial intersection analysis combined with maps of tea plantation phenological information was performed to obtain a tea plantation distribution map. The resulting tea plantation map had a high accuracy, with a 94% producer accuracy and 95.9% user accuracy. The method was also applied to Sentinel-2 images at the regional scale, and the obtained tea plantation distribution map had an accuracy of 88.7%, indicating the good applicability of the method.Keywords
Funding Information
- Natural Science Foundation of Zhejiang Province (LQ19D010010)
This publication has 37 references indexed in Scilit:
- Coastal wetland classification with multiseasonal high-spatial resolution satellite imageryInternational Journal of Remote Sensing, 2018
- Quantifying spatial-temporal changes of tea plantations in complex landscapes through integrative analyses of optical and microwave imageryInternational Journal of Applied Earth Observation and Geoinformation, 2018
- Photogrammetric Engineering & Remote Sensing, 2018
- Economic benefit and ecological cost of enlarging tea cultivation in subtropical China: Characterizing the trade-off for policy implicationsLand Use Policy, 2017
- Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based ApproachRemote Sensing, 2017
- Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classificationGIScience & Remote Sensing, 2016
- Detection of Drought-Induced Hickory Disturbances in Western Lin An County, China, Using Multitemporal Landsat ImageryRemote Sensing, 2016
- The Tea Industry and a Review of Its Price Modelling in Major Tea Producing CountriesJournal of Management and Strategy, 2016
- Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment techniqueInternational Journal of Remote Sensing, 2013
- Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imageryRemote Sensing of Environment, 2013