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1. Introduction |
Remote sensing technology is developing rapidly as an efficient method for acquiring data from a distance, usually from outer space by a satellite. It is now becoming a popular tool for natural resource surveys, which determine the distribution and quantity of various natural resources over a specific area, such as soil, grassland, forest, and farmland. Remote sensing is making this process quicker, more efficient, and more economical, by providing a wide range of imageries varying in resolution and spectral band, and with the help of land cover classification and spatial analysis techniques [ |
1 |
, |
2 |
, |
3 |
]. |
The first task to be completed before conducting a remote sensing-based survey is choosing an appropriate remote sensing data source to determine the imagery resolution and spectral bands, which have a significant effect on the accuracy of land cover classifications [ |
4 |
]. The influence of spectral bands on the extraction and visualization of ground features, which are physical objects on the ground such as forest and water, is well known. For example, red and near-infrared bands are used to calculate the Normalized Difference Vegetation Index (NDVI) for vegetation detection or different band combinations of Landsat Thematic Mapper (TM) imagery are employed to highlight different ground features. However, the selection of the resolution, or the size of pixels that constituting an imagery, remains an unsolved problem. There is no “one size fits all” solution [ |
5 |
], because the resolution should be determined with respect to the size of ground features, and there is one optimal resolution for each ground feature. If a unique resolution is adopted for all the ground features, details of objects smaller than the resolution would be lost. |
Currently, several methods are used for determining the size of ground features such as semivariograms, local variance (LV), wavelet method, and spatial autocorrelation [ |
6 |
, |
7 |
, |
8 |
]. Among them, semivariograms are widely adopted because of their mathematical simplicity and ease of interpretation. The |
range |
of a semivariogram is related to the size of the ground feature. It provides a measure for the size of the elements in the image and has been suggested to be a useful indicator in selecting the optimal spatial resolution [ |
7 |
, |
9 |
]. Woodcock and Strahler analyzed and validated the feasibility of semivariogram in determining the size of ground features in remote sensing using both simulated and real satellite imagery [ |
7 |
, |
10 |
]. Subsequently, semivariograms were applied in many remote sensing based studies, for example, to obtain the structure of forests from high-resolution remote sensing images [ |
11 |
, |
12 |
, |
13 |
]. Song and Liu studied the performance of semivariograms with respect to obtaining the canopy size via IKONOS or Quickbird imagery [ |
14 |
, |
15 |
]. Guardiola developed a new methodology for modeling spatial variations of the relative wood density using variograms for XRCT images [ |
16 |
]. |
One limitation of the application of semivariograms in remote sensing is that they can only be applied to simple scenes, which merely contain one element and one background [ |
7 |
, |
10 |
], while real satellite imagery tends consist of complex scenes that comprise multiple elements. The studies mentioned above mainly focused on merely one specific ground feature, such as grassland, forest, or canopy, and to meet simple scene requirements. Two main approaches below are used to solve this. Subimageries containing simple scenes were carefully extracted from the original imagery. This approach is called sub-area method (SAM) in this paper. Another way to solve this issue is separating those elements from one another using land cover classification techniques [ |
17 |
], which is called direct-analysis method (DAM) in this paper. Both methodologies have advantages and disadvantages. A more adaptive and reliable approach is needed for natural resource surveys. |
Another limitation is the massive sample size introduced using imagery. In traditional domains, such as mining and geology, the sample size for calculating a semivariogram is limited, and usually remains under 1000 [ |
18 |
]. However, in the case of remote sensing, the area of interest is entirely sampled, and each pixel in the imagery serves as a sample. As a result, the sample size can easily reach one million (for a common 1000 by 1000 pixels image), which is overlarge and makes building semivariogram computationally unrealistic. Apparently, the more samples included in the calculation, the more stable and accurate the estimate will be. However, more samples also mean higher requirements on the computing and memory capacities; therefore, not all pixels can be incorporated and the reliability of the analysis results might thus be impaired. Finding the balance between the sample size, computing capacity, and accuracy would facilitate the application of semivariogram in remote sensing. Unfortunately, there is no clear guide on it. |
This study proposes a Monte Carlo simulation-based approach (MCS) to enhance the performance of semivariograms in obtaining the optimal resolution for general ground features in conjunction with RapidEye imagery. First, the approach is compared with two other commonly used semivariogram-based methods for optimal resolution acquirement. The MCS is then applied to optimize the parameters ( |
sample size |
, |
maximum distance |
, |
and number of distance groups |
), as well as the model and number of simulation. Finally, the average size and the optimal resolution remote sensing images for three general ground features (grassland, farmland, and forest) in three counties in different geographic locations of China are obtained using the optimized parameters. The aim of this study is to improve the performance of semivariogram by reveling the influence of those parameters on the estimate accuracy and providing optimized parameters, to make |
range |
estimates more accurate and precise for the purpose of selecting appropriate remote sensing image for natural resource survey, in spite of the size of imagery used and the capacity of the computer. |