Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images

Abstract

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas. IntroductionLand degradation and desertification is a serious ecological and environmental problems and has received worldwide attention [1][2][3]. Percentage vegetation cover (PVC) represented with the range of values from 0.0 to 1.0 in this study is one of the effective indicators for assessing land degradation and desertification in arid and semi-arid areas and has been widely used. However, collecting field measurements of PVC in remote and sparsely populated arid and semi-arid areas is labor-intensive and time-consuming [4]. This method works for small areas only, which cannot provide the detailed information of spatial characteristics and temporal trend of PVC at a regional or global scale. Compared with the traditional method, remote sensing technologies can repeatedly offer images that cover a same region and quantify the spatial variability and temporal dynamics of PVC. Moreover, mapping PVC using remotely sensed images al...

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“…Usually, sample plots are randomly separated into training samples and testing samples [65], but collecting enough samples for modeling and validation is not easy, because of the high cost and limited accessibility. The k-fold cross validation method is useful for both classifications and estimation without extra data required (where, k is the number of sample plots).…”

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

“…Moreover, random forest (RF) is a nonparametric algorithm based on regression trees that can also be utilized to estimate PVC [16]. RF uses randomly selected training samples and variable subsets…”

“…The results of spectral unmixing analysis vary depending on many factors, such as landscape complexity and the used methods, images and spatial resolutions, selection of endmembers, and so on [5,6,9]. Various sensor and spatial resolution images have been used for PVC estimation [1,5,9,[11][12][13][14][15], but medium spatial resolution multispectral data are more commonly utilized because they are cheap and easy to obtain [5,11,14,16]. High spatial resolution images, such as those from IKONOS, QuickBird, RapidEye, Worldview, and Gaofen-2, can clearly reflect the features of vegetation canopies because of small pixel sizes and relatively small portions of mixed pixels, but are often only used for small areas due to their high costs [11].…”

“…Zhu et al [45] improved the measure of spectral similarity by calculating the weighted spectral distance based on correlations among the spectral variables used. Sun et al [16] further proposed an improved kNN by finding and using an optimal number of nearest neighbors, k, for each of the estimated locations. Compared with ANN and RF, this method is simpler and cheaper.…”

“…However, the estimation accuracy of all ANN algorithms varies depending on the size and characteristic representation of training samples. Generally, the larger the sample size and the better the representation of the training samples, the greater the estimation accuracy that can be achieved.Moreover, random forest (RF) is a nonparametric algorithm based on regression trees that can also be utilized to estimate PVC [16]. RF uses randomly selected training samples and variable subsets…”