Study on the Spatio-Temporal Changes of Tea Planting Area Based on Multi-Source Remote Sensing

doi:10.13304/j.nykjdb.2023.0243

Abstract

Abstract:

Tea is one of the important economic tree species. Studying the spatial and temporal changes of tea production is of great significance for optimizing the tea layout in Henan Province. Based on Landsat and Sentinel-2 satellite data from 2000 to 2020， combining with field survey data， the random forest classification method was used to obtain the spatial distribution information of tea area in Shihe District， Xinyang city of Henan province. Trend analysis， center of gravity migration and other methods were used to analyze the spatial distribution and temporal and spatial trends of tea production in Shihe District. The results showed that the combination of spectrum， vegetation index and texture feature had the highest classification accuracy of tea， with the overall accuracy of 89.02% and Kappa coefficient of 0.90. In terms of temporal changes， tea production in Shihe District from 2000 to 2020 generally showed a trend of rapid growth to steady rise. In terms of spatial change， the distribution of tea in Shihe District was relatively concentrated， mainly distributed in hilly areas and shallow mountainous areas in northern towns. From 2000 to 2020， the gravity center migration direction of tea area in Shihe District showed a trend from east to west. The total distance of gravity center migration was 5.47 km， and the migration speed was 0.273 5 km·a^-1. This study provides scientific data support and theoretical basis for the scientific planting of tea in Shihe District.

Key words: tea, area, multi-source, remote sensing, temporal and spatial changes

摘要：

茶叶是重要的经济树种之一，分析茶叶种植面积的时空演变规律可为优化河南茶叶布局提供科学依据。基于2000—2020年Landsat和Sentinel-2卫星数据，结合实地调查数据，采用随机森林分类方法获得河南省信阳市浉河区茶叶种植面积空间分布信息，采用趋势分析、重心迁移等方法对浉河区茶叶生产空间布局与时空变化趋势进行分析。结果表明：光谱+植被指数+纹理特征组合对茶叶分类精度最高，总体精度可达89.02%，Kappa系数为0.90；从时序变化来看，2000—2020年浉河区茶叶种植面积总体上呈现快速增长到稳步上升趋势。从空间变化来看，浉河区茶叶种植空间分布相对集中，主要分布于北部乡镇的丘陵地区和浅层山区。从重心变化来看，近20年浉河区茶叶种植面积重心呈现出由东向西转移的趋势，重心迁移总距离为5.47 km，迁移速度为0.273 5 km·a^-1。研究结果为茶叶科学种植提供了数据支撑。

关键词: 茶叶, 面积, 多源, 遥感, 时空变化

CLC Number:

S126

Yan ZHANG, Laigang WANG, Jia HE, Yan GUO, Xiuzhong YANG, Hongli ZHANG, Ting LIU. Study on the Spatio-Temporal Changes of Tea Planting Area Based on Multi-Source Remote Sensing[J]. Journal of Agricultural Science and Technology, 2024, 26(12): 107-114.

张彦, 王来刚, 贺佳, 郭燕, 杨秀忠, 张红利, 刘婷. 基于多源遥感数据的茶叶种植面积时空变化研究[J]. 中国农业科技导报, 2024, 26(12): 107-114.

Figures/Tables 6

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影像类型 Image type	空间分辨率 spatial resolution/m	影像数量 Number of images	采集年份 Collecting years	用途描述 Directions for use
Landsat5	30	11	2000—2010	识别2000年、2005年、2010年茶叶种植区 Identify tea area in 2000， 2005 and 2010
Landsat7	30	3	2000	配合landsat5识别2000年茶叶种植区 Identify tea area in 2000 with landsat5
Landsat8	15	6	2015—2020	识别2015年和2020年茶叶种植区 Identify tea area in 2015 and 2020
Sentinel-2	10	3	2015—2020	配合landsat8识别2015年和2020年茶叶种植区，并对结果进行精度验证 Identify tea area in 2015 and 2020 with landsat8， and verify accuracy of results

影像类型 Image type	空间分辨率 spatial resolution/m	影像数量 Number of images	采集年份 Collecting years	用途描述 Directions for use
Landsat5	30	11	2000—2010	识别2000年、2005年、2010年茶叶种植区 Identify tea area in 2000， 2005 and 2010
Landsat7	30	3	2000	配合landsat5识别2000年茶叶种植区 Identify tea area in 2000 with landsat5
Landsat8	15	6	2015—2020	识别2015年和2020年茶叶种植区 Identify tea area in 2015 and 2020
Sentinel-2	10	3	2015—2020	配合landsat8识别2015年和2020年茶叶种植区，并对结果进行精度验证 Identify tea area in 2015 and 2020 with landsat8， and verify accuracy of results

特征组合 Feature combination	总体精度 Total precision/%	Kappa系数 Kappa coefficient
光谱特征 Spectral feature	82.66	0.72
纹理特征 Texture feature	80.41	0.69
植被指数特征 Vegetable index feature	81.25	0.76
光谱+植被指数 Spectrum + vegetable index	86.76	0.83
纹理+植被指数 Texture + vegetable index	86.39	0.83
光谱+纹理 Spectrum + texture	86.02	0.82
光谱+纹理+植被指数 Spectrum+texture+vegetable index	89.02	0.90

特征组合 Feature combination	总体精度 Total precision/%	Kappa系数 Kappa coefficient
光谱特征 Spectral feature	82.66	0.72
纹理特征 Texture feature	80.41	0.69
植被指数特征 Vegetable index feature	81.25	0.76
光谱+植被指数 Spectrum + vegetable index	86.76	0.83
纹理+植被指数 Texture + vegetable index	86.39	0.83
光谱+纹理 Spectrum + texture	86.02	0.82
光谱+纹理+植被指数 Spectrum+texture+vegetable index	89.02	0.90

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