- 行業(yè)新聞
-
上海翻譯公司完成林業(yè)區(qū)域英文翻譯
發(fā)布時(shí)間：2020-09-25 08:16 點(diǎn)擊：
上海翻譯公司完成林業(yè)區(qū)域英文翻譯2.3.4.2 Remote sensing image classification
Remote sensing image interpretation is the process of analysis, reasoning and judgment based on feature information of various identify targets provided by remote sensing images, to identify the target surface features or phenomena, so as to obtain information target surface features. This study extracts remote sensing image information by combining computer initial classification and visual interpretation.The computer automatically classification is completed by combining ERDAS and eCognition. Based on classification techniques of spectral information facing pixel, ERDAS can extract vegetation information small surface features very well, and conduct image processing with unsupervised method (examples shown in Figure 2-6). Multi-scale image segmentation technology of eCognition makes up for defects on classification scale of remote sensing classification software, which is based on traditional spectral information facing pixel.
2.3.4.3 Visual interpretation
Modify initial classification results of remote sensing, and perfect land that are wrongly classified or left out. Specific operation is to identify target lands according to interpretation signs of images (Table 2-1) , such as hue (color), shape, location, size, shadow, texture, and other indirect signs and personal familiarity with the distribution of land in the region. At the same time, draw changed land type along the edge of image feature accurately (boundary should be strictly closed). The minimum unit of target surface features interpretation and extraction: according to general requirements, variable land should be larger than 3 × 3 pixels, the minimum width of shorter edge of pattern spot two pixels. The interpretation accuracy of pattern spot elements should be > 85.0%, visual interpretation line tracing accuracy should be two pixel points and keep mellow. Diagram shown in Figure2-7
Correction of TM data interpretation results
Figure 2-7 Picture of visual interpretation
2.3.4.5 Accuracy test
Conduct accuracy test on remote sensing classification results by fixed sample location data of continuous forest inventory in 1979 and 1992. The distribution of MSS, TM testing points are shown in Figure 2-8 and Figure 2-9. Accuracy of remote sensing classification is tested through the establishment of error matrix of land use types.
It can be seen from Table 2-2 and 2-3 that the classification accuracy of woodland and farmland is higher, while that of construction land and unused land is lower, because the minimum pattern spot of interpretation results is set to 90 * 90m2, resulting in information lost of construction land, the area of which is smaller than the set value; the reason that classification accuracy of unused land is low is because the definitions of unused land are different in two classification systems.
Table 2-2 Error matrix of MSS classification
Remote sensing classification results Woodland Farmland Grass land Water area Unused land Construction land Total
Sample plots dataWoodland 1184 278 160 1 18 1 1642 Farmland 368 1188 13 21 1 5 1596 Grass land 365 81 576 2 105 1129 Water area 14 23 4 23 3 67 Unused land 76 13 86 49 224 Construction land 27 47 7 3 4 88 Total 2034 1630 846 50 176 10 4746 Overall accuracy: 63.72% Mapping Accuracy Omission Accuracy User Accuracy Misclassification error Woodland 72.11% 27.89% 58.21% 41.79% Farmland 74.44% 25.56% 17.06% 82.94% Grass land 51.02% 48.98% 18.91% 81.09% Water area 34.33% 65.67% 2.00% 98.00% Unused land 21.88% 78.13% 10.23% 89.77% Construction land 4.55% 95.45% 1--% 9--% Kappa:47.56%（N=4746）
Table 2-3 Error matrix of TM classification
Remote sensing classification results Woodland Farmland Grass land Water area Unused land Construction land Total
Sample plots dataWoodland 2893 687 357 9 25 8 3979 Farmland 550 1730 15 20 2 34 2351 Grass land 183 8 533 8 20 1 753 Water area 16 45 3 51 2 2 119 Unused land 217 16 474 6 148 2 863 Construction land 22 71 0 7 0 14 114 Total 3881 2557 1382 101 197 61 8179 Overall accuracy: 65.64% Woodland 72.71% 27.29% 74.54% 25.46% Farmland 73.59% 26.41% 67.66% 32.34% Grass land 70.78% 29.22% 38.57% 61.43% Water area 42.86% 57.14% 50.50% 49.50% Unused land 17.15% 82.85% 75.13% 24.87% Construction land 12.28% 87.72% 22.95% 77.05%
2.4 Other statistics and survey data
Including statistical data of afforestation, forest management and other forestry activities, and statistical data of afforestation, harvesting, disease and pest, fire, etc. in forestry statistical yearbook (1978-2012), as well as statistical data of protection forest project in the upper reaches of the Yangtze River, natural forest protection project, reforestation project etc.
2.5 Land use transfer matrix
Land use transfer matrix obtained from the above data. For methodology of the first and second level, the matrix is land type transfer matrix during each inventory, for methodology of the third level, annual transfer matrix can be obtained. As an example, Table 2-4 shows land type transfer matrix during the 2007-2012 inventory. 世聯(lián)翻譯-讓世界自由溝通！專業(yè)的全球語(yǔ)言翻譯供應(yīng)商，上海翻譯公司專業(yè)品牌。絲路沿線56種語(yǔ)言一站式翻譯與技術(shù)解決方案，專業(yè)英語(yǔ)翻譯、日語(yǔ)翻譯等文檔翻譯、同傳口譯、視頻翻譯、出國(guó)外派服務(wù)，加速您的全球交付。 世聯(lián)翻譯公司在北京、上海、深圳等國(guó)際交往城市設(shè)有翻譯基地，業(yè)務(wù)覆蓋全國(guó)城市。每天有近百萬(wàn)字節(jié)的信息和貿(mào)易通過(guò)世聯(lián)走向全球！積累了大量政商用戶數(shù)據(jù)，翻譯人才庫(kù)數(shù)據(jù)，多語(yǔ)種語(yǔ)料庫(kù)大數(shù)據(jù)。世聯(lián)品牌和服務(wù)品質(zhì)已得到政務(wù)防務(wù)和國(guó)際組織、跨國(guó)公司和大中型企業(yè)等近萬(wàn)用戶的認(rèn)可。
