{"title":"利用航拍照片估算面积的最佳点网格密度","authors":"Lawrence R. Gering, Robert L. Bailey","doi":"10.1093/jof/82.7.428","DOIUrl":null,"url":null,"abstract":"Dot-count sampling to estimate area from 1:12,000 scale aerial photographs was tested with grid densities of 4, 16, and 49 dots per square inch. All three densities gave unbiased estimates. Variances may be validly estimated by assuming random sampling and the binomial model when dot density is greater than 27--the optimum density for per-dot reduction in variance. The coefficient of variation can be predicted from dot density of the grid and estimated proportion of dots in the cover type of interest.","PeriodicalId":15821,"journal":{"name":"Journal of Forestry","volume":"41 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimum Dot-Grid Density for Area Estimation with Aerial Photographs\",\"authors\":\"Lawrence R. Gering, Robert L. Bailey\",\"doi\":\"10.1093/jof/82.7.428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dot-count sampling to estimate area from 1:12,000 scale aerial photographs was tested with grid densities of 4, 16, and 49 dots per square inch. All three densities gave unbiased estimates. Variances may be validly estimated by assuming random sampling and the binomial model when dot density is greater than 27--the optimum density for per-dot reduction in variance. The coefficient of variation can be predicted from dot density of the grid and estimated proportion of dots in the cover type of interest.\",\"PeriodicalId\":15821,\"journal\":{\"name\":\"Journal of Forestry\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forestry\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1093/jof/82.7.428\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forestry","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1093/jof/82.7.428","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
Optimum Dot-Grid Density for Area Estimation with Aerial Photographs
Dot-count sampling to estimate area from 1:12,000 scale aerial photographs was tested with grid densities of 4, 16, and 49 dots per square inch. All three densities gave unbiased estimates. Variances may be validly estimated by assuming random sampling and the binomial model when dot density is greater than 27--the optimum density for per-dot reduction in variance. The coefficient of variation can be predicted from dot density of the grid and estimated proportion of dots in the cover type of interest.
期刊介绍:
The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry. The Journal is published bimonthly: January, March, May, July, September, and November.