DOI: 10.57760/sciencedb.j00001.00825
title: Spatial distribution data set of tea plantations with 10 m resolution from 2000 to 2020 in Fujian Province
title: 福建省2000-2020年10m分辨率茶园空间分布数据集

description: This dataset is a 10m resolution spatial distribution dataset of tea gardens in Fujian Province from 2000 to 2020, with a spatial resolution of 10m and a projection coordinate system of WGS_ 1984_ UTM_ Zone_ 50N. Set a time node every five years, including tea garden data for five years: 2000, 2005, 2010, 2015, and 2020. Store all results in a folder named "FJ_tea_10m". The file format in the package is shp's surface data, named "tea_date. shp", such as the 2020 tea garden spatial distribution data, named "tea_2020. shp".The processing process of this dataset mainly includes five parts: data preprocessing, feature extraction, feature optimization, tea plantation classification, and interference data removal method to obtain the temporal dataset:1. Data preprocessingS1 data is preprocessed using the Sentinel-1 toolbox, which includes calibrating orbit parameters, removing boundary and thermal noise, radiometric calibration, etc., and then synthesizing the image. The S2 data is mainly processed for cloud removal, resulting in 112 cloudless S2 images and synthesized. Use correlation functions in GEE to convert terrain data with a spatial resolution of 30m to a resolution of 10m, and then crop it according to the administrative boundaries of Fujian Province. Screen Landsat long time series images from July to October 2000 to 2020 on the GEE cloud platform, and use QA bands for cloud masking, replacing cloud cover data with neighboring months.2. Feature extractionThis dataset analyzed different data sources and constructed three different feature variables, including 26 spectral features, 68 texture features, and 4 terrain features.3. Feature SelectionIn order to obtain more accurate tea garden extraction results, four experimental schemes were designed in this dataset: spectral features, spectral features+texture features, spectral features+texture features+terrain features, and SVM_ RFE feature selection, where Scheme 4 is a combination of feature selection using SVM_ RFE Feature selection algorithm selects the most important feature variable for tea garden extraction, which avoids the problem of low extraction accuracy and efficiency caused by feature redundancy.4. Classification of tea gardensSupport vector machine classifier is used to classify tea garden data, and then Confusion matrix is used to evaluate the accuracy of the four classification schemes. The main reference values are producer accuracy, user accuracy, overall accuracy, and Kappa coefficient. The accuracy verification results show that Scheme 4, after feature optimization, has the highest extraction accuracy. Finally, a 10m resolution thematic spatial distribution map of tea gardens in Fujian Province was obtained in 2020.5. Obtaining Time Series Datasets Using Interference Data Exclusion MethodThrough field investigations, it was found that the area of tea gardens in Fujian Province has been continuously increasing in the past 20 years, and the distribution of tea gardens has been continuously expanding. Therefore, this dataset adopts the interference data removal method, using the obtained vegetation interference information to mask the distribution results of tea gardens in 2020 and earlier, and sequentially obtain the spatial distribution of tea gardens in 2000, 2005, 2010, and 2015.The specific steps for implementing the interference data removal method are: based on long-term Landsat series satellite data, the LandTrender algorithm is used on the GEE cloud platform to detect changes in Landsat temporal images, obtain the time nodes for vegetation disturbance and restoration, and divide the vegetation in Fujian Province into interference and non interference areas. Set up an interference node every 5 years to merge vegetation interference information, and obtain vegetation interference information from 2000 to 2004, 2005 to 2009, 2010 to 2014, and 2015 to 2019, respectively. Taking the steps to obtain spatial distribution data of tea gardens in 2015 as an example: based on prior knowledge of tea garden expansion, overlay analysis was conducted using vegetation interference information from 2015 to 2019 and 2020 tea garden special topics, removing patterns within the scope of tea garden special topics that overlap with interference information, and obtaining spatial distribution data of tea gardens in 2015. After performing overlay analysis and elimination operations on earlier tea garden data in the above manner, a temporal dataset of tea garden spatial distribution for the years 2000, 2005, and 2010 was obtained.After on-site investigation and verification, it was found that although tea gardens have shown a gradual expansion trend, the annual changes are very small, with only a few counties and cities experiencing relatively more changes in tea gardens. And this dataset uses tea garden data with a resolution of 10m in 2020 as the mask object. After being masked and imported into Google Earth for verification, it was found that tea garden data before 2020 is basically close to a resolution of 10m. Therefore, it can be considered that the resolution of tea garden data obtained through interference data removal method from 2000 to 2015 is 10m.
description: This dataset is a 10m resolution spatial distribution dataset of tea gardens in Fujian Province from 2000 to 2020, with a spatial resolution of 10m and a projection coordinate system of WGS_ 1984_ UTM_ Zone_ 50N. Set a time node every five years, including tea garden data for five years: 2000, 2005, 2010, 2015, and 2020. Store all results in a folder named "FJ_tea_10m". The file format in the package is shp's surface data, named "tea_date. shp", such as the 2020 tea garden spatial distribution data, named "tea_2020. shp".The processing process of this dataset mainly includes five parts: data preprocessing, feature extraction, feature optimization, tea plantation classification, and interference data removal method to obtain the temporal dataset:1. Data preprocessingS1 data is preprocessed using the Sentinel-1 toolbox, which includes calibrating orbit parameters, removing boundary and thermal noise, radiometric calibration, etc., and then synthesizing the image. The S2 data is mainly processed for cloud removal, resulting in 112 cloudless S2 images and synthesized. Use correlation functions in GEE to convert terrain data with a spatial resolution of 30m to a resolution of 10m, and then crop it according to the administrative boundaries of Fujian Province. Screen Landsat long time series images from July to October 2000 to 2020 on the GEE cloud platform, and use QA bands for cloud masking, replacing cloud cover data with neighboring months.2. Feature extractionThis dataset analyzed different data sources and constructed three different feature variables, including 26 spectral features, 68 texture features, and 4 terrain features.3. Feature SelectionIn order to obtain more accurate tea garden extraction results, four experimental schemes were designed in this dataset: spectral features, spectral features+texture features, spectral features+texture features+terrain features, and SVM_ RFE feature selection, where Scheme 4 is a combination of feature selection using SVM_ RFE Feature selection algorithm selects the most important feature variable for tea garden extraction, which avoids the problem of low extraction accuracy and efficiency caused by feature redundancy.4. Classification of tea gardensSupport vector machine classifier is used to classify tea garden data, and then Confusion matrix is used to evaluate the accuracy of the four classification schemes. The main reference values are producer accuracy, user accuracy, overall accuracy, and Kappa coefficient. The accuracy verification results show that Scheme 4, after feature optimization, has the highest extraction accuracy. Finally, a 10m resolution thematic spatial distribution map of tea gardens in Fujian Province was obtained in 2020.5. Obtaining Time Series Datasets Using Interference Data Exclusion MethodThrough field investigations, it was found that the area of tea gardens in Fujian Province has been continuously increasing in the past 20 years, and the distribution of tea gardens has been continuously expanding. Therefore, this dataset adopts the interference data removal method, using the obtained vegetation interference information to mask the distribution results of tea gardens in 2020 and earlier, and sequentially obtain the spatial distribution of tea gardens in 2000, 2005, 2010, and 2015.The specific steps for implementing the interference data removal method are: based on long-term Landsat series satellite data, the LandTrender algorithm is used on the GEE cloud platform to detect changes in Landsat temporal images, obtain the time nodes for vegetation disturbance and restoration, and divide the vegetation in Fujian Province into interference and non interference areas. Set up an interference node every 5 years to merge vegetation interference information, and obtain vegetation interference information from 2000 to 2004, 2005 to 2009, 2010 to 2014, and 2015 to 2019, respectively. Taking the steps to obtain spatial distribution data of tea gardens in 2015 as an example: based on prior knowledge of tea garden expansion, overlay analysis was conducted using vegetation interference information from 2015 to 2019 and 2020 tea garden special topics, removing patterns within the scope of tea garden special topics that overlap with interference information, and obtaining spatial distribution data of tea gardens in 2015. After performing overlay analysis and elimination operations on earlier tea garden data in the above manner, a temporal dataset of tea garden spatial distribution for the years 2000, 2005, and 2010 was obtained.After on-site investigation and verification, it was found that although tea gardens have shown a gradual expansion trend, the annual changes are very small, with only a few counties and cities experiencing relatively more changes in tea gardens. And this dataset uses tea garden data with a resolution of 10m in 2020 as the mask object. After being masked and imported into Google Earth for verification, it was found that tea garden data before 2020 is basically close to a resolution of 10m. Therefore, it can be considered that the resolution of tea garden data obtained through interference data removal method from 2000 to 2015 is 10m.