## 1 Introduction  
Earth Resources Technology Satellite (later renamed Landsat 1), launched on 23 July 1972 by the National Aeronautics and Space Administration (NASA), was the first earth-observing satellite explicitly designed for the study of planet Earth [1]. In 2008, the United States Geological Survey (USGS) offered the Landsat data, accessible via the internet for free [2]. Since then, the free and open data policy of the Landsat satellite missions has led to a rapid expansion of earth monitoring and operational applications [3, 4], and driven the adoption of similar policies by other countries, including the European Space Agency's Copernicus Program [5]. The Sentinel-2 mission satellites of the European Space Agency (ESA), after the launch of the first satellite on 23 June 2015, have expanded science and operational research by providing global acquisitions of high-resolution and high-revisit-frequency multi-spectral imagery [6, 7]. These high-resolution satellites are capable of observing Earth's land surface at spatial resolutions ranging from 10-60 m. Concurrently, many commercial Earth-imaging satellites have been developed and ultra-resolution images at sub-meter resolutions and very high-resolution images at sub-decameter resolutions are being offered by both the private and public sectors [8]. Well-known earth-observing satellites with the capability to acquire very high and ultra-resolution imagery include SPOT6/7 (panchromatic 1.5 m, multispectral 6 m), Pleiades-1A/B (panchromatic 0.5 m, multispectral 2.8 m), WorldView-1/2/3/4 (panchromatic 0.3-0.5 m, multispectral 1.24-1.84 m), and the SkySats constellation (panchromatic 0.57-0.86 m, multispectral 0.75-1 m) as some examples. The differentiation and mapping of land cover and vegetation types at a high-spatial resolution is important for better understanding of the earth systems, including climatic, biogeochemical, and hydrological processes [9; 10]. Several researchers have reported significant effects of the spatial resolution of remote sensing images in a number of cases, such as identification of land cover classes [11], tree species and forest classification and mapping [12; 13; 14], and assessment of forest fragmentation and ecosystem characterization [15; 16], as some examples. One of the major problems associated with very high-resolution satellites is that they operate in a narrow swath width and a trade-off exists between the spatial resolution of the acquired images and the revisit time (temporal resolution) of the satellite [17]. Plants exhibit seasonal characteristics such as onset of leaves, active growing period and leaf fall, etc. [18; 19]. Thus, the temporal resolution of very high-resolution satellites may become a bottleneck to retrieve seasonal information. To cope with this problem, effectiveness of the features extraction techniques that can augment additional information from the limited number of very high-resolution images such as radiometric indices [20], color transformations [21], and textural properties [22; 23] is expected. Researchers have reported better performance of the Hue-Saturation-Value (HSV) color space [24] over the Red-Green-Blue (RGB) color space for aerial or satellite imagery in land cover and vegetation-mapping applications including precise detection, classification, and mapping of crops and weeds [25; 26; 27], and detection and classification of urban vegetation and tree species [28; 29]. In addition, Haralick's textures [30], as a measure of the tonal variability between neighboring pixels, have provided significant improvements in a number of research areas such as discrimination of heterogeneous landscape vegetation types [31; 32; 33; 34], wetland vegetation classification [35], and detection of species-specific differences and intra-class separability [36; 37].