基于作物物候特征的水稻種植面積提取研究
發(fā)布時(shí)間：2018-12-24 16:00
【摘要】：“民以食為天”這句話突出了糧食對(duì)于人類生存的重要性。作為一個(gè)擁有近14億人口的發(fā)展中大國(guó),糧食安全是維系社會(huì)穩(wěn)定的前提。為確保我國(guó)糧食安全,國(guó)家和政府要堅(jiān)守18億畝耕地紅線。水稻作為我國(guó)最主要的糧食之一,對(duì)保障糧食安全有著至關(guān)重要的戰(zhàn)略意義。因此,及時(shí)準(zhǔn)確地掌握水稻種植面積、種植制度和生產(chǎn)管理方式等信息顯得尤為重要。隨著我國(guó)人口的不斷增長(zhǎng),城鎮(zhèn)化進(jìn)程加快,在這過(guò)程中不可避免的會(huì)出現(xiàn)占用耕地的情形,因而建立一個(gè)可靠的水稻面積檢測(cè)體系是十分必要的。農(nóng)業(yè)中原始獲取水稻種植面積的方法費(fèi)時(shí)費(fèi)力,而且有很大的局限性,而遙感手段在農(nóng)業(yè)中的運(yùn)用使得獲取作物種植面積的方法有了新的突破。大尺度水稻種植面積估算多以NOAA/AVHRR數(shù)據(jù)為基礎(chǔ),但其空間分辨率對(duì)于監(jiān)測(cè)有很大的限制。EOS/MODIS的出現(xiàn)相對(duì)于NOAA/AVHRR在空間分辨率方面有很大的提高,提高了在大尺度上進(jìn)行水稻種植面積估算的精度。本文以MODIS為數(shù)據(jù)源,在決策樹(shù)分類方法的基礎(chǔ)上結(jié)合水稻生長(zhǎng)過(guò)程中EVI值的變化情況和水稻物候期(移栽期、抽穗期、成熟期)對(duì)水稻的種植面積進(jìn)行識(shí)別,實(shí)現(xiàn)大尺度水稻種植面積的遙感提取。具體研究結(jié)果如下：(1)利用TIMESAT軟件中的Savitzky-Golay(S-G)濾波、非對(duì)稱高斯函數(shù)(AG)擬合、雙邏輯曲線(D-L)擬合對(duì)時(shí)間序列EVI植被指數(shù)進(jìn)行時(shí)間序列重構(gòu),減少數(shù)據(jù)中的異常值,提高數(shù)據(jù)可利用率。對(duì)經(jīng)過(guò)三種方法重構(gòu)后的曲線進(jìn)行對(duì)比分析、定量統(tǒng)計(jì)分析,最終選擇AG濾波作為EVI時(shí)間序列的重構(gòu)函數(shù)。(2)利用經(jīng)過(guò)AG濾波處理后的EVI時(shí)間序列提取水稻關(guān)鍵物候期(移栽期、抽穗期、成熟期)。將水稻生長(zhǎng)期過(guò)程中開(kāi)始的EVI最小值(EVImin)對(duì)應(yīng)的日期作為水稻的移栽期,將EVI曲線中的最大值(EVImax)出現(xiàn)的日期定為抽穗期,成熟期是通過(guò)相對(duì)閾值法確定的,EVI=EVImin+ΔEVI*0.6時(shí)對(duì)應(yīng)的日期定為成熟期。將提取的關(guān)鍵物候期的日期與氣象臺(tái)站觀測(cè)的日期進(jìn)行對(duì)比分析,發(fā)現(xiàn)大部分樣本值落在誤差為+16天的邊界內(nèi)。(3)將水稻生長(zhǎng)過(guò)程中的EVI值、水稻的關(guān)鍵物候期和一些統(tǒng)計(jì)量(生長(zhǎng)季長(zhǎng)度,EVI曲線振幅)作為決策樹(shù)中的分類條件,通過(guò)設(shè)置不同的閾值將整個(gè)研究區(qū)地物類型分為了水田、水體、林地、居民地和其他五類,并利用地表真實(shí)感興趣區(qū)獲得混淆矩陣,總體精度(Overall Accuracy)為94.37%,Kappa系數(shù)為0.9127,分類結(jié)果精度較高。將提取的水稻種植面積以市為單位進(jìn)行統(tǒng)計(jì)與統(tǒng)計(jì)年鑒記錄的當(dāng)年水稻播種面積對(duì)比分析,在36個(gè)市中,有27個(gè)市的精度在72.22%以上,但也有8個(gè)市的精度低于60%。從總體看,利用16天合成的MODIS-E VI數(shù)據(jù)對(duì)大尺度水稻種植面積進(jìn)行提取是可行的。
[Abstract]:The saying "the people take food as the sky" highlights the importance of food for the survival of mankind. As a developing country with a population of nearly 1.4 billion, food security is a prerequisite for maintaining social stability. In order to ensure our food security, the state and the government to adhere to 1.8 billion acres of arable land red line. As one of the most important grain in China, rice plays a very important strategic role in ensuring food security. Therefore, it is very important to grasp the information of rice planting area, planting system and production management. With the continuous growth of population and the acceleration of urbanization, it is inevitable to occupy cultivated land in the process, so it is necessary to establish a reliable rice area detection system. The original method of obtaining rice planting area in agriculture is time-consuming and laborious, and has great limitations. However, the application of remote sensing in agriculture has made a new breakthrough in the method of obtaining crop planting area. The estimation of large scale rice planting area is based on NOAA/AVHRR data, but its spatial resolution is very limited to monitoring. The appearance of EOS/MODIS is much higher than that of NOAA/AVHRR in spatial resolution. The precision of rice planting area estimation on large scale is improved. In this paper, MODIS is used as data source, based on the classification method of decision tree, the change of EVI value during rice growth and the phenological stage of rice (transplanting, heading and maturing) are combined to identify the planting area of rice. The large scale rice planting area can be extracted by remote sensing. The results are as follows: (1) using Savitzky-Golay (S-G) filter in TIMESAT software, asymmetric Gao Si function (AG) fitting, double logic curve (D-L) fitting to reconstruct time series EVI vegetation index. Reduce outliers in data and improve data availability. The curves reconstructed by three methods were compared and analyzed, and the quantitative statistical analysis was carried out. Finally, AG filter was selected as the reconstruction function of EVI time series. (2) the key phenology of rice (transplanting, heading and maturing) was extracted by EVI time series treated with AG filter. The corresponding date of EVI minimum (EVImin) in rice growing period was taken as the transplanting date of rice, the date of maximum (EVImax) in EVI curve was determined as heading date, and the mature period was determined by relative threshold method. The corresponding date of EVI=EVImin 螖 EVI*0.6 is set as maturity period. By comparing the date of the extracted key phenological period with the date observed by meteorological station, it is found that most of the sample values fall within the boundary of 16 days' error. (3) the EVI value in the rice growing process is changed. The key phenological period of rice and some statistics (length of growing season, amplitude of EVI curve) are used as the classification conditions in the decision tree. By setting different thresholds, the whole study area is divided into paddy field, water body and woodland. The confusion matrix is obtained by using the real area of interest of the earth surface and the other five categories. The overall accuracy of the confusion matrix is 94.37 kappa coefficient (0.9127), and the classification accuracy is high. The rice planting area extracted was compared with that recorded in the city and statistical yearbook. In the 36 cities, the precision of 27 cities was above 72.22%, but the precision of 8 cities was less than 60%. In general, it is feasible to extract large-scale rice planting area by using 16-day synthetic MODIS-E VI data.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S511;S127
本文編號(hào)：2390804
[Abstract]:The saying "the people take food as the sky" highlights the importance of food for the survival of mankind. As a developing country with a population of nearly 1.4 billion, food security is a prerequisite for maintaining social stability. In order to ensure our food security, the state and the government to adhere to 1.8 billion acres of arable land red line. As one of the most important grain in China, rice plays a very important strategic role in ensuring food security. Therefore, it is very important to grasp the information of rice planting area, planting system and production management. With the continuous growth of population and the acceleration of urbanization, it is inevitable to occupy cultivated land in the process, so it is necessary to establish a reliable rice area detection system. The original method of obtaining rice planting area in agriculture is time-consuming and laborious, and has great limitations. However, the application of remote sensing in agriculture has made a new breakthrough in the method of obtaining crop planting area. The estimation of large scale rice planting area is based on NOAA/AVHRR data, but its spatial resolution is very limited to monitoring. The appearance of EOS/MODIS is much higher than that of NOAA/AVHRR in spatial resolution. The precision of rice planting area estimation on large scale is improved. In this paper, MODIS is used as data source, based on the classification method of decision tree, the change of EVI value during rice growth and the phenological stage of rice (transplanting, heading and maturing) are combined to identify the planting area of rice. The large scale rice planting area can be extracted by remote sensing. The results are as follows: (1) using Savitzky-Golay (S-G) filter in TIMESAT software, asymmetric Gao Si function (AG) fitting, double logic curve (D-L) fitting to reconstruct time series EVI vegetation index. Reduce outliers in data and improve data availability. The curves reconstructed by three methods were compared and analyzed, and the quantitative statistical analysis was carried out. Finally, AG filter was selected as the reconstruction function of EVI time series. (2) the key phenology of rice (transplanting, heading and maturing) was extracted by EVI time series treated with AG filter. The corresponding date of EVI minimum (EVImin) in rice growing period was taken as the transplanting date of rice, the date of maximum (EVImax) in EVI curve was determined as heading date, and the mature period was determined by relative threshold method. The corresponding date of EVI=EVImin 螖 EVI*0.6 is set as maturity period. By comparing the date of the extracted key phenological period with the date observed by meteorological station, it is found that most of the sample values fall within the boundary of 16 days' error. (3) the EVI value in the rice growing process is changed. The key phenological period of rice and some statistics (length of growing season, amplitude of EVI curve) are used as the classification conditions in the decision tree. By setting different thresholds, the whole study area is divided into paddy field, water body and woodland. The confusion matrix is obtained by using the real area of interest of the earth surface and the other five categories. The overall accuracy of the confusion matrix is 94.37 kappa coefficient (0.9127), and the classification accuracy is high. The rice planting area extracted was compared with that recorded in the city and statistical yearbook. In the 36 cities, the precision of 27 cities was above 72.22%, but the precision of 8 cities was less than 60%. In general, it is feasible to extract large-scale rice planting area by using 16-day synthetic MODIS-E VI data.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S511;S127
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