|Zhang, Huihui - Texas A&M University|
|Lacey, Ron - Texas A&M University|
|Martin, Daniel - Dan|
|Bora, Ganesa - North Dakota State University|
Submitted to: Asian Conference on Precision Agriculture
Publication Type: Proceedings
Publication Acceptance Date: 10/10/2009
Publication Date: 10/10/2009
Citation: Zhang, H., Lan, Y., Lacey, R., Huang, Y., Hoffmann, W.C., Martin, D.E., Bora, G. 2009. Analysis of variograms with various sample sizes from a multispectral image. Asian Conference on Precision Agriculture. CDROM.
Interpretive Summary: Advanced remote sensing technologies possess a capability to instantaneously acquire vegetative reflectance data but new analytical methods are needed to characterize the spatial variability of crop conditions within the field. An integration multispectral camera was used to acquire airborne images for the spatial analysis of pest infestations. A study was undertaken to analyze different sample sizes of remotely sensed data. Spatial analyses were used to compute for the subset image chosen from a multispectral image, its NDVI image and various sizes of subsamples from them. By fitting with numerical models, characteristics of the spatial variability of vegetative reflectance were examined to study the changes with different sample sizes. The information will be particularly important as a guide for field sampling. Remote sensing technologies and spatial analysis techniques evaluated here will increase the speed of sampling and enhance the use of aerially-acquired images for precision application in pest management.
Technical Abstract: Variogram plays a crucial role in remote sensing application and geostatistics. It is very important to estimate variogram reliably from sufficient data. In this study, the analysis of variograms with various sample sizes of remotely sensed data was conducted. A 100x100-pixel subset was chosen from an aerial multispectral image which contains three wavebands, Green, Red and near infra-red (NIR). NDVI image also was derived from the subset image. These four image datasets were imported into R software for spatial analysis. Variograms were examined on these four full image datasets and sub-samples with design-based simple random sampling method. Half sample size of the subset image is enough to estimate the variogram for NIR and Red wavebands. To map the variation on NDVI within the weed area, sampling interval should smaller than 12 m. The information will be particularly important for kriging and also give a good guide of field sampling.