ArcGIS 9.3 software was downloaded from It includes Xinjiang Uyghur Autonomous Region, Qinghai Province, Gansu Province, Ningxia Hui Autonomous Region and Inner Mongolia Autonomous Region with eight famous deserts in China.
download erdas imagine 9.1 software testing
ArcGIS 9.3 software was downloaded from ERDAS IMAGINE 9.1 software was downloaded from -imagine.software.informer.com/9.1/. The result shows that Principal Component Analysis (PCA) image fusion algorithm is the best choice for GF-1 data.
Three-dimensional image generated from ArcGIS 9.3 software developed by ESRI (Environmental Systems Research Institute). ArcGIS 9.3 software was downloaded from Desertification means the ratio of annual precipitation to potential evapotranspiration falls within the range from 0.05 to 0.65; and evapotranspiration is highly related to temperature. Wind is the power of desertification. Meanwhile, population is one of the most important anthropogenic factors of desertification.
Pre-processing of satellite images is essential and aims at the unique goal of establishing a more direct linkage between the data and biophysical phenomena it represents (Abd El-Kawy et al. 2011; Coppin et al. 2004). Geometric correction was done using GPS data during field surveys. Image enhancement was used to adjust and improve specific visual qualities of the image using Histogram Equalization that was applied by ERDAS Imagine software. Map derivation from the images was done by unsupervised and supervised classification in Erdas Imagine 9.1 environment. The goal of the unsupervised classification is to achieve a general knowledge about the available LULC classes in the area as a result it proceeds without any choice of specific information about the features contained in any image (Giri et al. 2005; Giri 2012). The outputs of this stage will be used as a helpful aid in training samples. The ground referenced data required for image classification gathered by combining Google-earth and GPS points during the field survey. A signature file generated from ground reference data using the Signature Editor in ERDAS imagine 9.1. The Supervised Classification by contrast was done using the Maximum Likelihood Classifier (MLC) for each of the images separately. So multiband classes were derived statistically and each unknown pixel was assigned to the class determined by the MLC. The MLC tool considers both the variances and covariances of the class signatures when assigning each cell to one of the classes represented in the signature file (liu and Yetik 2010). The output of this stage is the LULC map of each image. Three LULC categories were extracted from TM, ETM+, and OLI images including those of agricultural land, barren/range land and forest land types.
Following image classification stage an accuracy assessment was performed for each image based on 260 points selected by stratified random method in ERDAS imagine 9.1 environment. Stratified random sampling techniques were readily accepted as the most appropriate method of sampling for resource evaluation studies that use remote sensing imagery data. This is because important minor categories are also satisfactorily represented (Genderen et al. 1978). Classification products were finally compared with reference data (GPS points, topographic maps and Google earth software) using Error Matrix before being used for evaluation.
From the previous section we can see there are a number of elements common to these citations including date, author, product name, version and URL. However, even this level of detail may not be adequate. For example, the software used might not be a release with a version number but a check-out from a source code repository, in which case, the version could be described in terms of a combination of a repository URL, check-out date, branch or tag name, or a revision number. The specifics would depend in part on the revision control system underlying the repository, as, for example, CVS uses file-specific version numbers, whereas Subversion uses repository-wide version numbers. Some software may not even have either a source code repository or a version number. In this case, such information as the download location and date become more important. In contrast, some software may be used as a service (e.g. a web service or RESTful end-point) rather than being downloaded and installed on a researcher's desktop. Finally, the software may not be accessible online but only via an e-mail to its author in which case the access date and contact details of the author become important.
Comparison of forestry cover between the solar tree and agrophotovoltaic system, (a) forestry landscape before solar power plant construction (b) non-forestry landscape after agrophotovoltaic system construction. See Fig. 2 for the location of the image. Time series images (2005 and 2020) were obtained from Google Earth Pro 7.3.4 ( -pro). The map created in Google Earth Pro 7.3.4, Erdas Imagine 9.1( -imagine.software.informer.com/9.1/) and Adobe Photoshop CS3 ( -photoshop-cs3-update.en.softonic.com/). 2ff7e9595c
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