Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets

Estimating Daily Maximum and Minimum Land Air Surface Temperature Using MODIS Land Surface Temperature Data and Ground Truth Data in Northern Vietnam

This study aims to evaluate quantitatively the land surface temperature (LST) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) MOD11A1 and MYD11A1 Collection 5 products for daily land air surface temperature (Ta) estimation over a mountainous region in northern Vietnam. The main objective is to estimate maximum and minimum Ta (Ta-max and Ta-min) using both TERRA and AQUA MODIS LST products (daytime and nighttime) and auxiliary data, solving the discontinuity problem of ground measurements. There exist no studies about Vietnam that have integrated both TERRA and AQUA LST of daytime and nighttime for Ta estimation (using four MODIS LST datasets). In addition, to find out which variables are the most effective to describe the differences between LST and Ta, we have tested several popular methods, such as: the Pearson correlation coefficient, stepwise, Bayesian information criterion (BIC), adjusted R-squared and the principal component analysis (PCA) of 14 variables (including: LST products (four variables), NDVI, elevation, latitude, longitude, day length in hours, Julian day and four variables of the view zenith angle), and then, we applied nine models for Ta-max estimation and nine models for Ta-min estimation. The results showed that the differences between MODIS LST and ground truth temperature derived from 15 climate stations are time and regional topography dependent. The best results for Ta-max and Ta-min estimation were achieved when we combined both LST daytime and nighttime of TERRA and AQUA and data from the topography analysis

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

Land surface temperature (LST) is one of the most important variables for applications relating to the physics of land surface processes. LST rapidly changes in both space and time, and knowledge of LST and its spatiotemporal variation is essential to understand the interactions between human activity and the environment. This study investigates the spatiotemporal variation of LST according to changes in elevation. The newest version (version 6) of MODIS LST data for 2015 was used. An area of 40,000 km2 (200 × 200 km2) in northwest Vietnam with elevations ranging from 8 m to 3165 m was chosen as a case study. Our results showed that the drop in LST with increased elevation varied throughout the year during both the daytime and nighttime. The monthly averages in 2015 and an altitude increase of 1000 m resulted in a decrease in LST ranging from 3.8 °C to 6.1 °C and 1.5 °C to 5.8 °C for the daytime and nighttime, respectively. This suggests that in any study relating to the spatial distribution of LST, the effect of elevation on LST should be considered. In addition, the effects of land use/cover and elevation distribution on the relationship between LST and elevation are discussed

Land cover classification maps of Mongolia from 2001 to 2020

The broad importance of land use and land cover information has been defined by and confirmed for many applications. Therefore, many land cover products have been developed at various scales (i.e., spatial resolution) and extensions (i.e., local, national, region, and global). Several studies have reported inconsistencies among global land cover (GLC) products causing that the accuracy of these products differ between regions. Recently, this issue has received a new level of attention, because many studies have pointed out that the inaccuracy of land cover products at regional scale can make a huge impact on the results of other applications relying on the GLC products (in the following downstream applications). Therefore, developing a method which can be easily and quickly applied to many different regions, but produce highly accurate land cover information is of utmost importance. To meet the first two criteria, several studies successfully used existing GLC products to automatically generate samples. However, none of these studies have been focused on the quality of the samples, which directly and largely affect the classification results. In this context, and taking Mongolia as a case study, we proposed a simple, fast, and accurate method to produce annual land cover maps with 250 m spatial resolution for entire Mongolia over a period of 20 years, from 2001 to 2020. The maps are based on MODIS data (products MOD13Q1 and MCD12Q1, version 6) and produced using modern machine learning techniques (the Random Forest) on the Google Earth Engine. Training samples have been selected by developing a semi-random approach which ensures that samples are spatially well-distributed, the number of samples for each class is in a similar order irrespective of the dominance of the land-cover classes and the samples are sufficiently apart from each other to reduce spatial autocorrelation. It is worth noting that we have selected Mongolia because of the low accuracy of GLC in this vast and remote country. Our results show that the accuracy of the new land cover maps improved compared with the corresponding MODIS products and if visually compared to Landsat images acquired at the same time. Overall accuracy from the validation data was approximately 90% for all new maps compared to 75% for the existing MODIS product. The result suggests that land cover maps, particularly for vegetation downstream application studies, can be largely improved based on the MODIS land cover products both regarding their spatial resolution and accuracy. Regarding Mongolia, these land cover maps are valuable e.g., for land degradation research, such as grassland monitoring, changes in forest cover, and monitoring desertification. Especially, information on grassland ecosystems is of utmost importance for Mongolia since more than half of the country economically depends on grassland resources. Therefore, Mongolia will benefit from the new dataset, not only economically, but also scientifically by helping researchers to discover more about the natural and social conditions of Mongolia. TIF file description: The land cover maps have 6 classes (land cover types), Water, Forest, Shrub land, Grassland, Others, and Bare land. Please note that water was masked out using the “JRC Global Surface Water Mapping Layers” (Pekel et al. 2016). LUC = Land use cover Spatial coverage: Mongolia (41.55709 - 52.17325 °N; 87.72279 - 120.04456°E

A simple, fast, and accurate method for land cover mapping in Mongolia

Low accuracy of global land cover (LC) products at local and regional scales is concerning, because of its huge impact on downstream applications. Therefore, developing a method for high accuracy LC maps at local and regional scale is of utmost importance. Taking Mongolia as a case study, we proposed a simple, fast, and accurate method to produce annual LC maps with 250 m spatial resolution from 2001 to 2020 using MODIS data. Our products have higher spatial resolution and higher accuracy (Overall Accuracy ∼90%) compared to MCD12Q1. These new maps are critical e.g., for land degradation research, and desertification/forest cover monitoring. Especially, information on grassland ecosystems is of utmost importance for Mongolia since more than half of the country economically depends on grassland resources. Therefore, Mongolia will benefit from the new dataset. Furthermore, due to the simplicity of the method, it can be easily applied and transferred to other regions

A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran

This paper examines the possible ecological controls on the diameter increment of oriental beech (Fagus orientalis Lipsky) in a high altitude forest in northern Iran. The main objectives of the study are computer-generated abiotic surfaces and associated plot estimates of (i) growing-season-cumulated potential solar radiation, (ii) seasonal air temperature, (iii) topographic wetness index in representing soil water distribution, and (iv) wind velocity generated from the simulation of fluid-flow dynamics in complex terrain. Plot estimates of the tree growth are based on averaged plot measurements of diameter at breast height increment during a growing period of nine years (2003–2012). Biotic variables related to the tree diameter increment involve averaged 2003 tree diameter and basal area measured in individual forest plots. In the modelling data (144 plots), the assemblage of modelled and observed site variables explained 75% of the variance in plot-level diameter increment. In the validation data (32 plots), the degree of explained variance was 77%. Mean tree diameter at breast height showed the strongest correlation with diameter increment, explaining 32% of the variation between-plot, followed by the configuration of topography and re-distribution of surface water (19.5%) and plot basal area (16.9%). On average, localised estimates of solar radiation and wind velocity potentially contribute to about 20% of the control on plot-level mean increment in oriental beech of the area. The results of the genetic programming showed that controlling the stand basal area and tree size by thinning and/or selective harvesting can have a favourable impact on the future distribution of mean diameter in oriental beech

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

Grassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large-scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi-scale and multi-temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area-wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel-derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients