list-type

The workflow in this study is shown in

Figure 3

. It includes three data processing sessions: (1) image interpretation; (2) method comparison; and (3) parameter optimization.

Image interpretation: In this session, an object-based automatic classification method, as well as the manual visual interpretation (manually identifying ground features according to prior knowledge on them, such as feature’s shape, size, pattern, tone, and texture), was used to identify farmland, built area, water, barren, grassland, and forest from remotely sensed images in eCognition software with its multiresolution segmentation algorithm [

26

]. First, some ground features such as farmland, built area, and water were visually interpreted, as they either mix easily with other adjacent ground features, such as farmland, or are hard to maintain their shape in the image segmentation, such as river (included in water). By doing this before the image segmentation, the shapes of those ground features could be maintained and high classification accuracy could be ensured. Image was segmented with a scale parameter (SP) of 200 into unclassified objects, and then those objects were classified based on different indexes, such as normalized difference soil index (NDSI), digital number (DN), normalized difference vegetation index (NDVI), brightness, and normalized difference water index (NDWI). The indexes and their value used for a specific ground feature could be found in

Table 3

. The value for those indexes were optimized through incremental adjustment to get the best result. Note that, the ground features in those counties were identified in the specific order listed in

Table 3

, and the order should not be changed, otherwise, different results may be derived. For accuracy assessment, a number of random points were generated, and Google Earth was adopted to check the points one by one manually.

Method comparison: The proposed MCS-based approach was compared with the sub-area method (SAM) and direct-analysis method (DAM) by applying them to the same area. A pilot area in Ansai County was selected to conduct the method comparison.

The SAM approach randomly selects 30 sample points for each ground feature (e.g., forest and grassland) using a stratified random sampling method based on land cover map. A rectangular area with a size of 500 by 500 pixels around each sample point was then created, which was called

sub-area

and used to extract

subimage

from the raw image. Those subimages in SAM are square and only cover a portion of a ground feature, they are then analyzed and the average of their estimated ranges will be calculated. Note that land cover map is not a must for SAM, subimages may be manually extracted from the raw image without land cover map and just based on individual’s judgement. However, this study used land cover map to improve efficiency of this step by automating this process.

The raw image was divided into five subimages based on the land cover map in the DAM. Therefore, there is one image for each ground feature. Different from the regular square subimages in SAM, the subimages in DAM were much larger, covering the entire area of a ground feature, and of irregular shapes. The images of different ground features are analyzed individually.

The MCS approach is built on the DAM. Instead of one-time analysis, MCS runs the analysis multi times to obtain a number of parallel estimations of the range. Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results [

27

]. One significant feature of this method is the large quantity of repetitions. Here, each simulation outputs a value for the estimated

range

and the average of all fitted ranges is considered to be the estimator of the true range. MATLAB equipped with a parallel-computing toolbox [

28

,

29

] was used to shorten the processing time by running several analyses simultaneously.

Parameter optimization: The semivariogram parameters, namely, the

sample size (SS)

,

maximum distance (MD)

, and

group number (GN)

, as well as the

model

and the simulation number, are to be optimized one by one through four assessment rounds.