本文選題：農(nóng)作物面積 + 遙感測(cè)量�。� 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：在第三次全國(guó)農(nóng)業(yè)普查的背景下,針對(duì)本次普查的主要工作,即對(duì)農(nóng)作物種植面積以及空間分布進(jìn)行全面準(zhǔn)確的調(diào)查,結(jié)合目前自動(dòng)化提取農(nóng)作物面積中存在的問題,本文試圖尋找一種有效地快速獲取大區(qū)域農(nóng)作物總面積測(cè)量的方法,為農(nóng)作物面積測(cè)算提供基礎(chǔ)數(shù)據(jù)支撐,從而為制定科學(xué)的農(nóng)業(yè)發(fā)展計(jì)劃提供科學(xué)依據(jù)。根據(jù)遙感測(cè)量的難易程度,本文設(shè)定了三個(gè)等級(jí)的測(cè)量區(qū),選取寧夏平原區(qū)、甘肅梯田區(qū)和貴州破碎區(qū)作為三個(gè)級(jí)別的代表區(qū)域。結(jié)合研究區(qū)物候數(shù)據(jù)以及高分一號(hào)(GF-1)WFV 16米衛(wèi)星影像數(shù)據(jù),選擇性獲取時(shí)相處于2016年3月-4月的國(guó)產(chǎn)衛(wèi)星影像作為農(nóng)作物面積遙感測(cè)量基礎(chǔ)數(shù)據(jù),數(shù)據(jù)源以GF-1為主,天繪一號(hào)衛(wèi)星(TH-1)為輔。為了提高遙感影像數(shù)據(jù)處理效率,針對(duì)GF-1和TH-1全色與多光譜數(shù)據(jù)同步獲取但匹配精度較差的特點(diǎn),選擇“先配準(zhǔn)融合、后正射糾正”的處理流程進(jìn)行遙感數(shù)據(jù)的批量、快速處理。以研究區(qū)為單位,分別制作正射影像成果。分別采用面向?qū)ο蟮挠?jì)算機(jī)自動(dòng)分類法對(duì)4波段正射影像數(shù)據(jù)、人工目視解譯法對(duì)真彩色合成后的3波段正射影像數(shù)據(jù)進(jìn)行農(nóng)作物遙感測(cè)量。最后以外業(yè)抽樣調(diào)查數(shù)據(jù)作為真值,從目視效果、測(cè)量精度以及測(cè)量時(shí)間三個(gè)方面,對(duì)兩種方法測(cè)得的農(nóng)作物面積測(cè)量結(jié)果進(jìn)行對(duì)比分析。通過以上研究,本文主要取得了以下成果:(1)通過對(duì)研究區(qū)作物播種前、生長(zhǎng)旺盛期、收獲后等多個(gè)時(shí)相的GF-1WFV 16米衛(wèi)星影像數(shù)據(jù)的充分對(duì)比、分析,并結(jié)合物候數(shù)據(jù),在單期高分辨率影像數(shù)據(jù)的基礎(chǔ)上,完成了農(nóng)作物遙感測(cè)量。由此,提出了一種充分利用GF-1WFV16米數(shù)據(jù)寬幅大、回訪周期短的特點(diǎn),結(jié)合GF-1 2米融合數(shù)據(jù)的高分辨率特點(diǎn),進(jìn)行快速、大區(qū)域農(nóng)作物面積測(cè)量的調(diào)查方法。(2)針對(duì)GF-1、TH-1數(shù)據(jù)特點(diǎn),選擇“先配準(zhǔn)融合、后正射糾正”的處理方式,充分結(jié)合各遙感數(shù)據(jù)處理軟件優(yōu)勢(shì),對(duì)DOM制作中的配準(zhǔn)、波段組合、融合、正射糾正、鑲嵌、色彩調(diào)整、裁切等各主要環(huán)節(jié)均總結(jié)、研制了一系列批量、快速處理方法,為今后進(jìn)行大規(guī)模農(nóng)作物遙感測(cè)量提供了海量DOM快速制作解決方案。(3)本文選取了面向?qū)ο蠓诸惡腿斯つ恳暯庾g兩種方法,分別對(duì)三個(gè)研究區(qū)進(jìn)行了農(nóng)作物遙感測(cè)量,并以外業(yè)抽樣調(diào)查數(shù)據(jù)作為真值,對(duì)兩種測(cè)量結(jié)果進(jìn)行了對(duì)比分析。研究表明:采用面向?qū)ο蠓诸惙ǐ@得的農(nóng)作物面積測(cè)量結(jié)果在空間分布上與人工目視解譯法基本一致;三類研究區(qū)的整體精度均能達(dá)到90%以上,能夠滿足應(yīng)用需求;而在處理速度上,面向?qū)ο蠓ㄏ啾热斯つ恳暯庾g法,可提高兩倍左右,且隨著測(cè)量面積的增加,其測(cè)量速度優(yōu)勢(shì)越明顯。因此,當(dāng)需要快速獲取大范圍農(nóng)作物遙感測(cè)量結(jié)果時(shí),采用面向?qū)ο蟮挠?jì)算機(jī)自動(dòng)分類法是一種比較好的選擇方式。
[Abstract]:In the context of the third National Agricultural Census, in view of the main work of this census, that is, to carry out a comprehensive and accurate survey of crop planting area and spatial distribution, combined with the problems existing in automatic extraction of crop area at present, This paper attempts to find an effective and rapid method for measuring the total area of crops in a large area, which provides the basic data support for the calculation of crop area and provides scientific basis for making scientific agricultural development plan. According to the degree of difficulty and ease of remote sensing measurement, this paper sets up three grades of measuring areas, including Ningxia Plain, Gansu terraced area and Guizhou broken area as the representative regions of the three levels. Combined with phenological data of the study area and Gaof-1 (GF-1) WFV 16m satellite image data, the domestic satellite images from March to April 2016 were used as the basic data of crop area remote sensing measurement. GF-1 was the main data source. Tianyi-1 satellite (TH-1) is auxiliary. In order to improve the efficiency of remote sensing image data processing, aiming at the feature that GF-1 and TH-1 panchromatic data are acquired synchronously with multi-spectral data, but the matching accuracy is poor, the batch of remote sensing data is selected as "registration fusion first, then forward correction". Quick processing. Taking the research area as the unit, the orthophoto image results were made respectively. The orthophoto data of 4 bands were classified by object oriented automatic classification method, and the 3 band orthophoto image data of true color were measured by artificial visual interpretation method. Finally, as the true value, the results of crop area measurement obtained by two methods are compared and analyzed from three aspects: visual effect, measuring precision and measuring time. Through the above research, this paper mainly obtained the following achievements: (1) by comparing and analyzing the GF-1WFV16m satellite image data of the crops in the study area before sowing, growing vigorous period and after harvest, and combining phenological data, Based on single phase high resolution image data, crop remote sensing measurement is completed. Therefore, this paper puts forward an investigation method to make full use of the wide width of GF-1WFV16m data and the short period of return visit, combined with the high resolution characteristics of GF-1 / 2m fusion data, to measure the area of crops in a fast and large area. (2) aiming at the characteristics of GF-1WFV16m data, Selecting the processing method of "first registration fusion, then forward correction", fully combining the advantages of each remote sensing data processing software, making registration, band combination, fusion, orthographic correction, mosaic, color adjustment in Dom production, Cutting and other major links are summarized, developed a series of batch, rapid processing methods, This paper provides a solution for large-scale crop remote sensing measurement in the future. (3) in this paper, two methods, object oriented classification and artificial visual interpretation, are selected to measure crops in three research areas. The data of field sampling survey were used as true value to compare and analyze the two kinds of measurement results. The results show that the measured results of crop area obtained by the object-oriented classification method are basically consistent with the artificial visual interpretation method in spatial distribution, and the overall accuracy of the three study areas can reach more than 90%, which can meet the needs of application. In terms of processing speed, the object-oriented method can increase the speed of measurement by about twice as much as the artificial visual interpretation method, and with the increase of the measurement area, the advantage of the measurement speed is more obvious. Therefore, when it is necessary to quickly obtain the results of crop remote sensing measurement on a large scale, it is a better choice to adopt the object-oriented computer automatic classification method.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S127;TP751

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