bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Beyond Traditional Methods: Innovative Integration of LISS IV and Sentinel 2A 2 Imagery for Unparalleled Insight into Himalayan Ibex Habitat Suitability 3 Ritam Dutta1,2,3, Bheem Dutt Joshi1, Vineet Kumar1, Amira Sharief1, Saurav Bhattcharjee1 4 Rajappa Babu3, Mukesh Thakur2, Lalit Kumar Sharma1,* 5 6 1 Zooological Survey of India, Prani Vigyan Bhawan, Kolkata, West Bengal 700053 7 2 University of Madras, Navalar Nagar, Chepauk, Triplicane, Chennai, Tamil Nadu 600005 8 3 Southern Regional Centre, Zoological Survey of India, Chennai, Tamil Nadu 600028 9 *Corresponding author: Lalit Kumar Sharma 10 lalitganga@gmail.com 11 12 Abstract: 13 Despite advancements in remote sensing, satellite imagery is underutilized in conservation 14 research. Multispectral data from various sensors have great potential for mapping 15 landscapes, but distinct spectral and spatial resolution capabilities are crucial for accurately 16 classifying wildlife habitats. Our study aimed to develop a technique for precisely discerning 17 habitat categories for the Himalayan Ibex (Capra sibirica) using different satellite imagery. 18 To address both spectral and spatial challenges, we utilized LISS IV and Sentinel 2A data and 19 integrated the LISS IV data with Sentinel 2A data along with their corresponding geometric 20 information. Employing multiple supervised classification algorithms, we found the Random 21 Forest (RF) algorithm to outperform others. The integrated (LISS IV-Sentinel 2A) classified 22 image achieved the highest accuracy, with an overall accuracy of 86.17% and a Kappa 23 coefficient of 0.84. 24 To map the suitable habitat of the Ibex, we conducted ensemble modeling using the 25 Land Cover Land Use (LCLU) of all three image types (LISS IV, Sentinel 2A, Integrated) 26 and other predictors such as topographical, soil type, vegetation, and water radiometric 27 indices. The integrated model provided the most accurate prediction of suitable habitat for the 28 Ibex, surpassing the other two LCLU classes derived from individual images. The Soil 29 Adjusted Vegetation Index (SAVI) and elevation were identified as crucial factors in 30 identifying suitable habitats. 31 These findings hold valuable implications for the development of effective conservation 32 strategies, as accurate classification schemes enable the identification of vital landscape 33 elements. By precisely classifying LULC satellite images and identifying crucial habitats for 34 the Ibex, this pilot study provides a new and valuable strategy for conservation planning. It bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 35 enhances our ability to preserve and protect the habitat of wildlife species in the mountain 36 ecosystem of the Himalayas. 37 Keywords: Image classification, Integration image, Ensemble species distribution model, 38 Himalayan Ibex. 39 1. Introduction: 40 Satellite imagery is full of detail and contributes significantly to the dissemination of 41 geographical information (Muhammad et al. 2012). The utilisation of satellite and remote 42 sensing images provides both quantitative and qualitative data, which streamlines fieldwork 43 and shortens research times (Chaichoke et al. 2011). Satellite remote sensing techniques 44 capture images at regular intervals. Satellites with various band ranges, geographical 45 resolutions, and spectral resolutions have made it possible to gather more types of remote 46 sensing data from the same area (Wang et al., 2001). The significance of proficient analysis 47 and processing of remote sensing images has been on the rise owing to the exponential 48 growth of remote sensing data (Yin et al. 2021). Because of its wide range of potential 49 applications in fields as diverse as geography, ecology, city planning, forest monitoring, and 50 the military, remote sensing image scene classification is receiving increasing amounts of 51 research and development funding (Cheng et al. 2017). There are several different types of 52 multiresolution and multispectral data now accessible. The data acquired through 53 multispectral remote sensing is characterised by narrow spectral bands that possess a 54 relatively larger bandwidth. Consequently, the gathered data can be employed to examine the 55 spatial characteristics of ground substances (Vohra and Tiwari 2020). The limitations of a 56 single source of satellite data in accurately extracting ground objects are attributed to spectral 57 resemblance among different objects or spatial proximity between the objects. Consequently, 58 for the enhancement of data evaluation precision, it is imperative to appropriately construe 59 object characteristics such as configuration and spatial interconnections, in conjunction with 60 the spectral response (Vohra and Tiwari 2020). 61 It has become a challenge in remote sensing technology to figure out how to combine 62 different sourced data and produce the most useful image possible. However, in recent years 63 have seen an increase in the importance of image fusion within image processing applications 64 as a result of the plethora of available acquisition methods. Integrating many images from 65 various sensors into a single, useful one for analysis, is the goal of image fusion, which is a 66 relatively a new field (Ma et al. 2019). The comprehension of digital image fusion techniques 67 can enhance the interpretation of multiresolution and multi-sensor data, resulting in improved 68 images that are more suitable for both human perception and seamless computer analysis bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 69 tasks such as extraction of features, segmentation, and object recognition (Luo et al. 2016; 70 Berger et al. 2015). The fusion of data from multiple sensors and resolutions has been found 71 to be beneficial in enhancing the quality of low-resolution data. Additionally, this approach 72 offers supplementary information collected from the same geographical location, which can 73 be better comprehended than relying solely on data from a single sensor (Ma et al. 2018). 74 Combining the spatial and spectral characteristics of remote sensing images, image fusion 75 technology has gradually broadened its application field, and now the fusion image concept 76 has been applied to land cover land use (hereafter LCLU) categorization. Recently, there has 77 been an explosion of interest in using multi-sensor data fusion for LCLU classification. 78 Accuracy in land cover feature categorization can be improved through the integration of data 79 from many remote sensing sensors with varying resolutions. The demand for greater 80 precision in image and data analysis has spurred research into multiresolution and multi- 81 sensor data, as well as improved methods for gaining access to higher-resolution remote 82 sensing data (Rodriguez-Galiano et al. 2012a). The biophysical state of the Earth's surface 83 and immediate subsurface is characterised by the composition of topography, soil, surface 84 water, forest, grassland, groundwater, marsh and human structures, collectively referred to as 85 "land cover." Furthermore, land use for recreational purposes, wildlife habitat, and 86 agricultural land can the example of land use (Turner et al. 1995; Weng 1999; Sherbinin 87 2002). 88 It is obvious that land cover classification, derived from remotely sensed data, is still a 89 crucial societal need for natural resource management, surveillance, and development 90 strategies (Colditz et al. 2011; Topaloğlu et al., 2016; Khatami et al., 2016). Numerous 91 studies have generated information from remote sensing by making LCLU maps from various 92 data sources like multispectral, hyperspectral and radar aperture (Craig Dobson et al. 1995; 93 Soria-Ruiz et al., 2010; Pal and Foody 2010; Szuster et al., 2011; Miettinen and Liew, 2011; 94 Srivastava et al., 2012; Hütt et al., 2016; Fonteh et., 2016; Wei et al., 2016; Büyüksalih, 95 2016; Mohajane et al., 2018; Sirro et al., 2018; Juliev et al., 2019, Camargo et al., 2019, 96 Zafari et al. 2019), subsequently, the LCLU types were classified through the utilisation of 97 machine learning algorithms (Patenaude et al. 2005; Rosenqvist et al. 2003; Wulder et al. 98 2018). This type of mapping is useful for assessing landuse dynamics, identifying ecosystem 99 services, understanding the effects of global climate change, and formulating land use policy 100 (Fry et al., 2011; Burkhard et al., 2012; Gebhardt et al., 2014; Guidici and Clark, 2017; Noi 101 and Kappas 2017; Hussain et al., 2020). bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 102 Furthermore, with the growing statistical models nowadays Species distribution models 103 (hereafter SDM), is a great conservation tool which enhance the capabilities of conservation 104 managers to delaminate conservation priority areas on the base of species presence and the 105 association with their environment. SDM can directs to finds appropriate conservation 106 policies, estimating the area under invasion, evaluate species richness of an area and estimate 107 the probable habitat of any species (Franklin, 2010). SDMs perform crucial role in 108 quantitively ecology by systematically inspecting a species’ relation, in terms of evaluating 109 the biotic and abiotic factors for distributing an organism in a given area (Franklin, 2010). 110 The SDMs approach was developed based on Hutchinson's ecological niche theory, initially 111 introduced in the 1950s and later revised by Booth et al. (1988). Within the ecological 112 context, the species exhibits significant interactions with various factors such as dietary 113 resources, vegetation types, elevation profiles, and climatic elements (Morris et al., 2012; 114 Besnard et al., 2013). The SDMs employ several methods to determine how a species' 115 presence in the environment affects that species' ability to choose an area on a spatial surface 116 that would be favourable for that species (Guisan and Thuiller, 2005; Franklin, 2010; 117 Peterson et al., 2011). It is quite challenging to determine whether a species is actually 118 absent, even though occurrence records can be obtained through museums, published 119 literatures, and field studies. Several SDM methods have been created that solely use positive 120 presence data in order to address this difficulty (Phillips et al., 2006). Instead of using only 121 one modelling technique, the ensemble modelling strategy uses multiple SDM models, which 122 increases the accuracy of predictions about a species' geographic range (Araújo and New, 123 2007; Thuiller et al., 2009; Marmion et al., 2009). Due of the ambiguity in selecting one 124 strategy from numerous, ensemble modelling is more effective to a single SDM technique 125 (Pearson et al., 2006; Elith and Graham, 2009; Buisson et al., 2010; Garcia et al., 2011). 126 Himalayan Ibex, also known as Himalayan Ibex is a member of Bovidae family is a 127 true goat species. This caprinae have a wide distribution range in the mountains of India, 128 Pakistan, China, Mongolia, Afghanistan, Tajikistan, Uzbekistan, Kazakhstan, Kyrgyzstan and 129 Russia (Shackleton 1997, Fox et al.1991, Fedosenko and Blank 2001). This species has a 130 wide residence which ranging from Karakoram and Hindu-Kush mountains of Pakistan 131 through the higher elevated areas of Lahaul-Spiti and some other patches of Himachal 132 Pradesh. The existence of this mountain goat species in this region since 1641 CE as per 133 Saini et al. 2019. This species has very wide range of habitat in its distributional range which 134 mainly configure by steep slopes, mountain ridges, rugged terrain, rocky outcrops, cold 135 deserts and foothills (Dzieciolowski et al. 1980; Clark et al. 2006, Khan et al. 2016). It is the bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 136 largest among the Capra genus, adult males can reach up to 5.6 feet and weigh up to 130 kg 137 (Fedosenko and Blank 2001). The Himalayan Ibex are affable, and its social groups are 138 composed of both single-sex and mixed-sex individuals, with the latter being observed only 139 during certain seasons. By affecting plant species composition, vegetation structure, and 140 nutrient cycling, ungulates play a significant role in preserving ecosystems (McNaughton 141 1979; Bagchi and Ritchie 2010). Therefore, maintaining and managing ungulate populations 142 and their habitat is a key goal of conservation management. Habitat is one of the prerequisites 143 components which supports any species to survive. Therefore, monitoring habitat is one of 144 the crucial elements which can help to make any conservation strategy. Himalayan Ibex is an 145 “Near Threatened” mammalian species as per IUCN Red list (Reading et al. 2020), and 146 categories as a Schedule I species under the Wildlife (Protection) Act, 1972 in India. This 147 caprinae species lost their habitat with the rapid urbanization, habitat destruction, hunting and 148 poaching. 149 The present study aims at the integration of LISS IV and Sentinel 2A images over the 150 Jispa valley of Lahaul Spiti district, Himachal Pradesh, which is under Trans-Himalayan 151 landscape. The purpose of this research is to combine high spatial and spectral data to better 152 distinguish different types of LCLU and find how the classification help to predict the habitat 153 of Himalayan Ibex in this landscape. 154 2. Study area: 155 The present study was carried out in the Jispa valley site. The landmass segment is located in 156 the eastern part of the Lahaul valley in the Lahaul Spiti district of Himachal Pradesh, India 157 (Fig 1). Total area of the study area encompasses 559 km2, and lies in UTM zone 43N. This 158 landmass falls under Trans Himalaya Ladakh Mountains (1A) Indian biogeographic zones, 159 which geomorphology is very distinct. High mountains, inclining slopes, and sparse 160 vegetation are the main features of this area. This area has only two clearly defined seasons. 161 High snowfall is frequent during the winter. Farming is one of the primary sources of income 162 in this area; the main commercial crops, which are only grown in the summer, are potatoes, 163 peas, cauliflower, and cabbage. This area is intersected by the River Bhaga. This landmass is 164 extremely important since it supports a variety of crops, biodiversity, pasture, linking roads, 165 and human settlements. 166 3. Methodology: 167 3.1. 168 3.1.1. Data Acquisition Land Class and Land Use Classification using multiple satellite imagery bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 169 In implementing this study, we assessed two different source satellites viz. Linear Imaging 170 Self-Scanning Sensor (LISS) IV and Sentinel 2A. LISS IV is a multi-spectral sensor with 171 high resolution and data providing in three spectral bands (viz. B2 0.52 µm - 0.59 µm, B3 172 0.62 µm - 0.68 µm, B4 0.77 µm - 0.86 µm). A ground resolution of 5.8 m (at nadir) provides 173 by LISS-IV. Moreover, a rotating deck is attached to a Payload Steering Motor mounted with 174 LISS-IV, which can revolve by 26 degrees and allow for a 5-day revisit of any given ground 175 region. Since the system has 10-bit quantization and can cover 100% of the albedo with a 176 single gain, no gain commands are necessary. Furthermore, Sentinel 2A is an optical 177 multispectral imaging mission with a wide-swath and high resolution. The Global Navigation 178 Satellite System (GNSS), a dual-frequency receiver with an orbital accuracy-specific 179 propulsion system, assists in maintaining each satellite's position in orbit. The multispectral 180 optical instrument of the Sentinel 2A satellites contains 13 spectral bands (Visible, Near- 181 Infra-Red and Short Wave Infra-Red) with spatial resolutions of 10 m, 20 m, and 60 m for the 182 various spectral bands with a 290 km swath width. The satellite's sun-synchronous orbit is at 183 a mean altitude of 786 km, and it completes a 5-day cycle with the two satellites (Drusch et 184 al., 185 (https://bhoonidhi.nrsc.gov.in/) (Table 1) and Sentinel 2A (Table 1) Level-1C multispectral 186 instrument 187 (https://earthexplorer.usgs.gov/). 188 The exact geometric correction and registration of two images is the most fundamental 189 requirement for accurate image classification (Balcik, F.B. and Sertel, E., 2002). Sentinel 2A 190 data was pre-processed in the Sentinel Application Platform (SNAP Desktop, Version 6.0.0) 191 for resolution enhancement of the bands by the highest resolution of the other bands and all 192 the bands were converted into the same resolution (Brodu,2017). The satellite image band 193 was stacked in ArcGIS 10.6 environment with layer stacked function. The amalgamation of a 194 higher spatial resolution image and a higher spectral resolution image is advantageous for 195 remote sensing research (Chitade and Katiyar, 2012). Image fusion usually involves two main 196 processes, (a) Image to image geometric registration of two datasets geometry and (b) 197 combining spectral and spatial data to produce a new, enriched dataset that differs from the 198 originals (Lu and Weng, 2007). 2012). LISS scenes IV were imagery downloaded was from collected United from States Bhoonidhi Geological portal Survey 199 Image to image geometric registration is the process of superimposing images (two or 200 more) of the same scene that were captured at various times, from various angles, and or by 201 various sensors (Brown, 1992). The image-to-image geometric registration method was 202 assigned to the satellite images in order to run an accurate fusion procedure. We have used bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 203 the Coregistration QGIS plugin (Scheffler et al. 2017) for the procedure, where we use the 204 LISS IV image as a reference image and the Sentinel 2A image as a target image. 205 3.1.2. Integration of the two different satellite images: 206 As we mentioned, we used SNAP for the Sentinel 2A bands to convert the 20 meters bands at 207 the lowest resolution i.e., 10 meters. After that, we applied the nearest neighbour resampling 208 method (Wenbo et al. 2008) to minimize the loss of spectral information of Sentinel 2A 209 images and to gain a spatial resolution of 5 meters, like the LISS IV images. Furthermore, in 210 the integrating image the three bands of LISS IV assembled with the Sentinel 2A bands by 211 the replacement of the 3 bands of Sentinel 2A (i.e., B3, B4, B8; Green, Red and NIR bands 212 respectively). Since, the LISS IV has B2, B3 and B4 (Green, Red and NIR, respectively) 213 bands and the B2, B3 and B8 of Sentinel 2A bands have a similar spectral resolution. After 214 the integration of the LISS IV and Sentinel 2A bands total bands were 10, now it has good 215 spatial and spectral resolution as well as it gains bands number. 216 3.1.3. Image Classification: 217 The extraction and interpretation of valuable information from enormous satellite images 218 necessitate an effective and efficient statistical methodology. Image classification is a method 219 that involves the categorization of each pixel in an image or raw information obtained from 220 remote sensing satellites, with the aim of generating an appropriate set of labels for specific 221 land cover themes (Lilles and, Keifer 1994; Abburu and Golla 2015; Karlsson 2003). 222 In supervised satellite image classification methods, the training sample is the most crucial 223 element. Analyst input is required for supervised classification methods. The fundamental 224 process of supervised classification involves the examination of input data, generation of 225 training sets and signature files, and the assessment of the training sets and signature files' 226 efficacy (Abburu and Golla 2015). 227 In this study we classify the images (LISS IV, Sentinel 2A, and the integrated image) 228 by five supervised machine learning classification techniques viz. Maximum Likelihood, K- 229 nearest neighbourhood, Support Vector machine, Guassian mixed model and Random Forest 230 algorithm. In this present study the images classified into nine classes of LCLU types: 231 agriculture land, sparse vegetation, barren land, scrub areas, juniper patch, settlements, 232 permafrost, water bodies and road ways. The training data utilised in this classification 233 process was gathered for all eight LCLU classes, with the exception of permafrost, through 234 field surveys conducted within the study landscape. We have used the same training polygons 235 to classify the LISS IV, Sentinel 2A as well as the integrated image. bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 236 The idea is to evaluate the performance of various satellite images using their bands 237 as variables and to calculate the precision of mapping quantification by applying remote 238 sensing data to actual ground-truth circumstances. The accuracy evaluation of each defined 239 class is determined by an error matrix that compares map information with reference data and 240 the sampled area or points. Such errors are attributed to the accuracy of the producer and user 241 (Congalton and Green 2019; Foody 2002). The accuracy is derived from a final classification 242 error matrix made up of many multivariate statistical studies, where the overall accuracy, 243 kappa, and F-statistics represent the accuracy of various classified classes (Congalton et al., 244 1983; Congalton 1991; Foody 2002; Nitze et al. 2012; Keshtkar et al. 2017, Gumma et al. 245 2019). 246 As a parametric statistical and supervised classification method, the Maximum 247 Likelihood (hereafter ML) classifier is the most popular technique used in remote sensing 248 applications (Jia et al. 2011). This approach uses an average with values, variance, and 249 covariance classification technique that is based on statistics and takes into account the values 250 of the variables (Günlü 2021). However, Gaussian mixture model (hereafter GMM) is 251 another machine learning algorithm. They are employed to divide data into various groups in 252 accordance with the probability distribution. GMMs uses to classify satellite images based on 253 probabilistic concept (Gu et al. 2007; Chellappa et al. 2009; Okwuashi et al., 2011; Lakshmi 254 et al. 2015). One of the simplest machine learning and supervised learning methods is the K- 255 nearest neighbour algorithm (hereafter KNN). 256 classifying objects by utilising the proximity of training samples in the feature space 257 (Bremner et al., 2005, Nurwauziyah et al., 2018). Support Vector Machine (hereafter SVM) 258 is essentially a supervised machine learning technique. The structural risk minimization 259 concept and statistical learning theory serve as the foundation for the SVM (Günlü 2021). 260 This algorithm is typically used to address classification-related challenges, while it can also 261 be used to address regression-related complications. SVM differentiates between the objects 262 based on the separation of the hyper-planes (Huang and Zhang,2012, Luo et al., 2015, 263 Melgani and Bruzzone 2004). The KNN algorithm is a method for 264 It has been proved that Random Forest (hereafter RF) is capable of producing 265 accurate LCLU maps (Rodriguez-Galiano et al., 2012b; Sonobe et al., 2014). RF constructs 266 decision trees before randomly combining them (Breiman 2001; Liaw & Wiener, 2002; 267 Ishwaran & Kogalur, 2007; Ishwaran et al., 2008; Noi and Kappas 2017, Sonobe et al., 2017). 268 The supervised classification of these three images performed by dzetsaka classification tool, 269 SCP tool in QGIS and ArcGIS 10.6 (Esri 2018, Karasiak 2019, Congedo 2021) bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 270 3.2. 271 3.2.1. Occurrence locations of the Himalayan Ibex: 272 During the extensive field study during 2019-2022 we recorded the occurrence of Himalayan 273 Ibex. The collection of the occurrences of the species’ we followed camera trapping method, 274 trail sampling, vintage point and questionnaire survey. We have sampled all types of habitats 275 in the search of Himalayan Ibex. However, the study area composed of rugged terrain, steep 276 slopes, high mountains, uncertain weather conditions so representative sampling was carried 277 out. During the field work we gathered 167 presence locations, furthermore we used spatially 278 autocorrelated 82 locations for final analysis. 279 3.2.2. Variables preparation and selection: 280 The variable selection for SDM is the critical stage in this analysis because the variables 281 should be plausible for the Himalayan Ibex presence. The Trans-Himalayan landscape is 282 mostly rugged and found gradients in elevation with less amount of vegetation growth which 283 makes this area complex and this complexity also observe in the habitat selection by wild 284 animals like Himalayan Ibex. We used Digital elevation model data from Alos Palsar at 12.5- 285 meter spatial resolution and used as primary source for derived slope and aspect. The LCLU 286 classes were generated from the good accuracy produced by classified images. Furthermore, 287 radiometric indices of soil, vegetation and water computed from Sentinel 2A image. All the 288 data was rasterized and resampled at same spatial scale i.e. at 5 meter by the help of spatial 289 analyst extension tool of ArcGIS 10.6. Primarily we prepared 23 ecological pertinent 290 variables for this study, however, for the final model building only selected uncorrelated 291 variables through Pearson correlation coefficients (r) higher than 0.8. 292 3.2.3. Selection of performing models and evaluation of modeling algorithms: 293 Because of the complex and varied nature of the association between species and their 294 environmental predictors, studies indicate that no single modelling approach is best in every 295 circumstance (Marmion et al., 2009). Modelling algorithms broadly classified as 296 Classification models, Regression models and Complex models. Hence, we applied 297 Multivariate adaptive regression splines (MARS) and Generalized linear model (GLM) from 298 Regression models, Boosted regression trees (BRT) from Classification models and 299 Maximum Entropy Model (MaxEnt), Random Forest (RF) from Complex models, each form 300 with 10-fold cross-validation (Hayes et al. 2015, Dutta et. al 2022). We developed the 301 modelling workflow using the SAHM module by utilising the VisTrails pipeline; using this 302 method, models were able to choose the predictors that best describe the model performance 303 (Morisette et al., 2013; Talbert and Talbert, 2012, Dutta et al. 2022). Each model generated Habitat Suitability modelling for Ibex bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 304 an estimate of the potential habitat suitability for every pixel, presented as continuous values 305 ranging from 0 to 1, which was subsequently interpreted to represent the probable habitat 306 suitability for a specific pixel in the current study area. The predicted habitat suitability was 307 determined through the binary maps, with consideration given to the minimal training 308 occurrence as a threshold (Hayes et al., 2015). We generated 3 ensembled maps as follows, 309 LISS IV derived LCLU and other topographic and radiometric variables at 5-meter spatial 310 resolution, Sentinel 2A derived LCLU and other topographic and radiometric variables at 10 311 meter spatial resolution, Integrated image derived LCLU and other topographic and 312 radiometric variables at 5 meter spatial resolution. The average of all of the binary 313 predictions (0 or 1) of the five models are used in the ensemble maps for each map pixel, 314 moreover the count surface depicts the model agreement, where 0 refers no model agreement 315 and 5 refers all model agreement for suitability estimation (Dutta et al. 2022). 316 For the purpose of comparing the performance of SDMs, numerous performance 317 metrics are frequently utilised. We employ area under the receiver operating characteristic 318 curve (AUC), Cohen's Kappa, True Skill Statistic (TSS), Proportion Correctly Classified 319 (PCC), sensitivity, and specificity to more clearly grasp comparative SDM performance 320 across the 5 model forms (Cohen, 1968; Allouche et al., 2006; Phillips 2010; Illán et al. 2010; 321 Jiménez-Valverde et al. 2013). Generally, AUC considered as a threshold-independent 322 statistic to evaluate models (Guisan and Zimmermann 2000; Brotons et al. 2004; Elith et al. 323 2006; Phillips et al. 2006; Pearson 2007; Grenouillet et al. 2011). We used the minimal 324 training presence threshold for metrics (specificity and sensitivity), which are threshold- 325 dependent (Peterson et al. 2011). The criterion for building ensemble model was based on 326 AUC threshold value of >0.75 of CV dataset. Variable importance was evaluated by the mean 327 AUC, which is the AUC values ratio and calculated by number of models runs for every 328 model. 329 4. Result: 330 4.1. 331 The outcome of the three classified images (LISS IV, Sentinel 2A and the integrated image) 332 quality assessment by different accuracy assessments have been discussed in the following 333 sections. The improvements in the integrated image have been examined by comparing the 334 integrated products with the LISS IV and Sentinel 2A images. The integrated image has a 5 335 metre spatial resolution like LISS IV, but it also improved spectral signature. We derived 336 nine LCLU classes of the study area from the three types of images with five different 337 supervised classification algorithms (Fig 2). 810 sample points (90 points for each class) Classification of Land Class and Land Use bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 338 across the study area were obtained by the stratified random sampling approach to evaluate 339 the classification accuracy of the images. The study region consists of several LCLU features 340 such as sparse vegetation, scrub covers, juniper patches, road lines, water, agriculture field, 341 human settlements, barren land, permafrost area. Many of the aforementioned properties are 342 challenging to extract from LISS IV sensor data because of its fine resolution, which fail to 343 distinguish many classes at once. With the aid of integrated data, the classification result 344 increases and the features identified more accurately. As a result, fused data might be a cost- 345 effective alternative to better resolution multispectral data. The classification accuracy of 346 integrated classified images was compared to that of LISS IV and Sentinel 2A classified 347 images. Classification accuracy estimated by overall accuracy (OA), Kappa coefficients (κ) 348 Weighted Kappa, Producer’s accuracy (PA), User’s accuracy (UA), commission, omission 349 and F-statistics (F) (Table 2, 3, S1 – S4). (Congalton 1991). 350 classification accuracy not good by LISS IV image, no classifying algorithm perform well, 351 the highest overall accuracy achieved by SVM (OA = 60.61) and κ statistic calculated was 352 0.56, and the lowest performance by GMM algorithm (OA= 48.76). and κ statistic calculated 353 was 0.42, so the two classifying algorithms achieve moderate accuracy (Table 2, Fig S1 -S5) 354 (Landis and Koch 1977). Moreover, the other classier viz. ML, RF and GMM classifier 355 overall accuracy score was 57.40, 56.41, 48.76 and κ statistic score was 0.52, 0.51, 0.42 356 respectively that directed also moderate accuracy on the LISS IV image (Table 2, Fig S1 - 357 S5). Result depicts that the 358 The accuracy estimation of the classification algorithm on Sentinel 2A imagery 359 evaluate that the RF model classify the area with highest overall accuracy i.e., 80.24 and κ 360 statistic 0.78 and the lowest performance by KNN algorithm i.e., 70.24 and κ statistic 0.67, so 361 the results show substantial agreement (Table 2, Fig 2, Fig S1 -S5). Other algorithms viz. 362 SVM, ML and GMM performing OA score 76.54, 73.08 and 71.35 respectively and the κ 363 statistic score was 0.73, 0.70, 0.68, respectively which is also substantial accurate as per 364 Landis and Koch (1977) (Table 2, Fig 2, Fig S1 -S5). Interestingly, the integrated image 365 shows higher classification accuracy compared to LISS IV and Sentinel 2A image (Table 2, 366 Fig 2, Fig S1 -S5). The highest overall accuracy produces by RF i.e., 86.17 and κ statistic 367 0.84 which is a perfect accuracy estimation (Table 2, Fig 2, Fig S1 -S5). KNN classifier also 368 perform great with the integrated image classification than the LISS IV and Sentinel 2A with 369 an overall accuracy value of 81.85 and κ statistic 0.80 and again it qualifies as perfect 370 classification (Table 2, Fig 2, Fig S1 -S5). The other classification algorithm such as the 371 SVM, ML and GMM predicted overall accuracy are 76.79, 71.72, 67.77 respectively and κ bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 372 statistic 0.74, 0.68, 0.64 respectively, which qualify as substantial classify (Table 2, Fig 2, Fig 373 S1 -S5). From this overall accuracy and κ statistic it is clear that integrated image 374 classification performs well than the single sensor images. However, the weighted κ also 375 depicts the same (Table 2, Fig S1 -S5). 376 However, the accuracy estimation for identifying each feature class from these images we 377 calculated producer’s accuracy, user’s accuracy F- statistics, omission and commission error 378 rates. Regarding to the comparison of each feature class, permafrost class differentiate by 379 each classification algorithms on every image perfectly. Omission error rates depicts that the 380 highest pixels misclassified by GMM algorithm on LISS IV image (Table 3, Table S1 -S4, 381 Fig 2, Fig S1 – S5). Road, settlement and sparse vegetation classes are poorly classified by 382 this algorithm on the LISS IV image (Table 3, Table S1 -S4, Fig 2, Fig S1 – S5). However, 383 the commission error rate directs that the road, highly misclassified by ML classification on 384 the LISS IV image (Table 3, Table S1 -S4, Fig 2, Fig S1 – S5). However, classification of 385 LISS IV image by ML, GMM, SVM poorly classify settlement, scrub, juniper patch, barren 386 and agriculture classes (Table 3, Table S1 -S4, Fig 2, Fig S1 – S5). The result of producer’s 387 accuracy shows that road, settlement and sparse vegetation, agriculture and water poor 388 accuracy in terms of classified by GMM algorithm on LISS IV image (Table 3, Table S1 -S4, 389 Fig 2, Fig S1 – S5). The user’s accuracy and F- statistics depicts that the road, settlement and 390 sparse vegetation, agriculture and water class gained highest accuracy with the integrated 391 image classified by RF algorithm (Table 3, Table S1 -S4, Fig 2, Fig S1 – S5). Furthermore, 392 the KNN classifier on integrated image and RF classifier on Sentinel 2A image having 393 overall accuracy score >= 80%, κ statistic and weighted κ score >=0.80, which tells perfect 394 object classification but the best classification by RF on the integrated image among all the 395 classification (Table 3, Table S1 -S4, Fig 2, Fig S1 – S5). 396 4.2. 397 After applying supervised classification on these satellite imageries and evaluate the accuracy 398 of image classification we used these LCLU to evaluate the potential habitat distribution of 399 Himalayan Ibex, in this study landscape. Habitat suitability of a species can address by its 400 environment where the species thrive. The LCLU one of the important aspects which tells 401 about the habitat of the species. Performing the species distribution model is crucial for 402 conservation of species in their natural habitat. Our model builds with 82 uncorrelated 403 occurrence records of Himalayan Ibex. Furthermore, we assigned the LCLU derived from the 404 best classified image of three different images (Sentinel 2A, LISS IV and Integrated image). 405 Only LCLU are not enough when the species resides in complex and rough terrain. So, we Habit Suitability Ensemble model bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 406 choose topographical variables and soil, vegetation and water radiometric indices along with 407 the LCLU for knowing the suitable habitat of this species. Classified LCLU maps helps to 408 understand the habitat more precisely, fine resolution can provide more insights to the habitat 409 of the species. However, we selected 21 predictors at starting (Table S5), after the 410 multicollinearity test, we removed correlated variables and use only variables which follow 411 the Pearson’s rule i.e., <0.8 (Fig S6 – S8). The AUC values of five different modelling 412 algorithm ranged from 0.77-0.92 when habitat class use from LISS IV classified image, AUC 413 values ranged from 0.77-0.91 when habitat class use from Sentinel 2A classified image and 414 when integrated classified image derived habitat class use for the suitability prediction the 415 AUC value ranged from 0.77-0.92 for training data sets, which reflects great performance by 416 the models (Table 4, Fig 3). The model performance also evaluated by other metrices like 417 True skill statistic (TSS), Percent Correctly Classified (PCC), Cohen's Kappa, specificity and 418 sensitivity and depicts good performance by the models (Table 4, Fig S9 – S14). The 419 development of the final ensemble model and ensemble count maps was achieved as every 420 participation model satisfied the AUC requirement of 0.75 and above (Fig 3, 4). The LISS IV 421 derived LCLU with other variables show most suitable area about 63.80 km2, the Sentinel 2A 422 derived LCLU with other variables show most suitable area about 72.42 km2 and the 423 Integrated image derived LCLU with other variables show increase in most suitable area 424 about 78.42 km2 (Table S6, Fig 6, 7). In all instances when the rest of the model uses some of 425 the variables, RF and MaxEnt methods used all uncorrelated variables. Furthermore, for all of 426 the models with all of the combinations, the Soil Adjusted Vegetation Index (SAVI) with the 427 highest mean AUC, found as a major contributor and most significant variable among the 428 variables (Fig S15 – S32). Different LCLU shows significance differently in the three types 429 of images (Fig S15 – S32). In the LISS IV image the Barren is the leading variable which 430 influence in the model building (Fig S15, S18-22), moreover Juniper patch shows higher 431 significance among the LCLU while using the Sentinel 2A and Integrated image (Fig S16 - 432 17, S23-32). When the Himalayan Ibex occurrence is more prevalent, the response curve for 433 these variables has high peaks, and the likelihood values fall as the distance increases (Fig 18 434 - 32). 435 5. Discussion: 436 The theme of this research was to compare the object classification methods derived from 437 different sensor imagery and the preparation of an integrated image and analysis the 438 classification performance of it, for the improvement of LCLU analysis which play pivotal 439 role for species habitat configuration. Two different sensors namely LISS IV and Sentinel bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 440 2A, were considered for the preparation of the integration image. The images were classified 441 by 5 different type of supervised classification technique viz. ML, GMM, KNN, SVM and 442 RF. As a result, five LISS IV, five Sentinel 2A and five integrated images were produced 443 (Fig 2). In total fifteen images were compared with each other by qualitatively measures 444 (Table 2, 3, S1-S4, Fig S1-S5). Consequently, all of the images were subjected to visual 445 interpretation and comparison. Subsequently, the integrated images revealed discrete 446 outcomes that evince enhancements of varying magnitudes (Table 2, 3, S1-S4, Fig S1-S5). 447 The results of the qualitative evaluation indicate that RF produced the most advantageous 448 result on integrated images, while GMM produced the least favourable results on LISS IV 449 images (Table 2). 450 Classified images by integration techniques were investigated also by comparing the 451 classification results of original LISS IV and Sentinel 2A images for each selected class 452 (Table 2, 3, S1-S4, Fig S1-S5). The results demonstrate a clear improvement in classification 453 when utilising the RF method on the integrated image. However, KNN, SVM and ML of 454 Integrated showed great accuracy estimation for the image classification. Moreover, classifier 455 algorithms perform also well in the Sentinel 2A image after integrated image (Table 2, 3, S1- 456 S4, Fig S1-S5). In the case of LISS IV image, the classifier did not perform good, which 457 easily interpret by the accuracy estimation. Among the all models GMM classification on 458 LISS IV image was the worst (Table 2, 3, S1-S4, Fig S1-S5). In a nutshell it has been 459 determined that the utilisation of multi-sensor data significantly improves the precision of 460 LCLU classification, resulting in a more dependable and superior map generation. The 461 integration of two different satellite image result showed promising result to differentiate the 462 LCLU types. All the supervised classification methods used to classify the images discussed 463 in this paper are capable of properly classify multispectral images with good accuracy except 464 LISS IV. Furthermore, some model imperfectly classify settlement, road, water bodies which 465 account by visual interpretation (Fig 2). 466 The habitat suitability model of Himalaya Ibex, derived from the best classification 467 method of three images depicts there is a change in most suitable habitat (Table S6, Fig 4-5). 468 From this study we conclude that in this rugged terrain the species have complex association 469 with habitat which can not only defined by land classes, it also highly influenced by the 470 physical character of the terrain (Fig S15-S32). From the distribution model assessment, we 471 found that SAVI and elevation have major role which shape their habitat (Fig S15-S32). Area 472 with little vegetation cover, SAVI is used to adjust the NDVI for the impact of soil 473 brightness. So, the improperly classified image of LISS IV predict least suitable area bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 474 followed by Sentinel 2A classified image (Table S6, Fig 4-5). Furthermore, the integrated 475 image derived LCLU with the other topographic and radiometric variables predict an increase 476 in suitable area (Table S6, Fig 4-5). SDM with all image derived LCLU and other variables 477 predicts 34.76 km2 common (Table S6, Fig 4-5). Whereas, 41.50 km2 area common in LISS 478 IV and Sentinel 2A derived LCLU SDM model, 44.91 km2 area common in LISS IV and 479 integrated image derived LCLU SDM model and 54.14 km2 area common in Sentinel 2A and 480 integrated image derived LCLU SDM model (Table S6, Fig 4-5). Therefore, the study's 481 findings show that an integrated image can distinguish LCLU more effectively than a single 482 satellite image of the two images employed in this study. The present study evaluates the 483 performance of the SDM in conjunction with the three image-derived LCLU datasets, 484 incorporating both topographical and radiometric variables. The results indicate that the SDM 485 exhibits favourable performance across all LCLU datasets. Notably, the integrated image 486 dataset demonstrates a high level of precision in identifying LCLU, which may prove useful 487 in characterising the habitat configuration of the Himalayan Ibex. 488 5.1. 489 The current study deal with the comparison between the contribution of LISS IV, Sentinel 490 2A, and the integrated image of these aforementioned multisensor image with five 491 classification algorithms for the enhancement of LCLU analyses. After that, we select the 492 best classified image of these three different types of images for extraction of LCLU and 493 predict suitable habitat of Himalayan Ibex and compare which is the best among them. 494 For the image classification we employed five supervised classification algorithms, namely 495 ML, GMM, KNN, SVM and RF. Subsequently, statistical analysis and visual interpretation 496 were performed on each image. Due to the heterogeneous landscape's complexity, the classes 497 can occasionally be difficult to distinguish; nevertheless, the integrated image classified by 498 RF method significantly enhanced the mapping of each class. The F- statistics, Producers 499 accuracy, user's accuracy, commission and omission rate show that the classes classify with 500 low accuracy scores in LISS IV and Sentinel 2A images, however, employing integration to 501 improve their accuracy. In conclusion, the integrated image showed unique findings with 502 varying degrees of improvement. For both visual and qualitative analysis of the classified 503 images, RF demonstrated the best results on integrated image and GMM classification on 504 LISS IV was the worst. From the accuracy assessment the second-best classifier algorithm 505 was KNN on integrated image. However, it is evident that Sentinel 2A image also perform 506 good to classify the ground objects but no classifier algorithm could not classify LISS IV 507 image at a significant way. Conclusion: bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 508 Furthermore, this investigation reinforces the efficacy of SDM ensemble modelling in 509 the prioritisation of conservation strategies and management. We recommended ensemble 510 model, against using a single modelling technique, particularly for species with complex 511 habitat. Due to the geomorphological complexities of this study area, only LCLU is not 512 sufficient to predict this species suitable habitat, so the topographic and radiometric variables 513 useful for this study. However, it can conclude that how the specific topographic factor with 514 LCLU supports the habitat of the species potential distribution in this study area, because the 515 landcover classes distributed in specific altitudinal gradients. The maximum models use 516 elevation and SAVI for prediction of suitable habitats in this area. However, we can infer 517 from this study that topographical characteristics may serve as a proxy for identifying 518 suitable habitat of mountainous species when LCLU classification is not fair, hence the 519 poorly classified image of LISS IV also shows fair suitable areas of the species. 520 Himalayan Ibex is a mountain ungulate which help to shape the ecosystem by many 521 dimensions. The snow leopard, the apex predator in this Trans Himalayan environment, relies 522 on this species of mountain ungulate as one of its primary food sources. (Suryawanshi et al. 523 2017, Sharief et al. 2022). Furthermore, the indigenous inhabitants of this region have 524 several kinds of traditional beliefs regarding Himalayan Ibex. 525 We therefore draw the conclusion from this study that all types of images can be 526 helpful for species distribution models in this type of complicated terrain when they are 527 associated with other crucial and influential variables like elevation, SAVI, slope, and aspect. 528 However, a precise description of LCLU can help to know the distribution of suitable habitat 529 in space more significantly, which helps to understand where the focus for conservation 530 needs to be placed for the long-term survival of wild animals. With this finding, we suggested 531 a community conservation area in the study landscape because it is clear that there is a 532 considerable amount of habitat for this wild mountain goats in this area, despite the fact that 533 there is no such protected area. 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Evaluating the performance of a random forest kernel for land cover classification. Remote sensing, 11(5), p.575. 876 877 bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 878 Table 1: Data type and acquisition details for the two satellite imagery. S.No Satellite/Sensor Product Sensing Row Path Date type . orbit of Resoluti Tile No / acquisition number Sentinel 2A L1C 105 ---- ---- 2021-10-06 on Product ID (meter) (LISS IV) 10, 20, T43SFS 60 Sentinel 2A L1C 105 ---- ---- 2021-10-06 10, 20, T43SGS 60 LISS IV 879 ---- ---- 048 095 2020-09-02 5 221881311 Table 2: Accuracy assessment of LISS IV, Sentinel 2A and Integrated images classified by five supervised classifiers namely, ML (Maximum Likelihood), GMM (Guassian mixed model), KNN (K-nearest neighbourhood), SVM (Support Vector machine) and RF (Random Forest). The evaluation metrices are Overall accuracy, Kappa (κ) statistic, SE of Kappa (κ) statistic and Weighted Kappa (κ). 883 SE of Weighted Kappa Kappa Overall Kappa ( ) ( ) accuracy ( ) statistic statistic Accuracy Assessment κ κ κ 884 885 Image type LISS IV Sentinel 2A Integrated LISS IV Sentinel 2A Integrated LISS IV Sentinel 2A Integrated LISS IV Sentinel 2A Integrated ML GMM 57.41 73.08 71.73 0.52 0.70 0.68 0.02 0.02 0.02 0.51 0.71 0.71 KNN 48.77 71.35 67.78 0.42 0.68 0.64 0.02 0.02 0.02 0.41 0.65 0.61 SVM 55.31 70.24 81.85 0.50 0.67 0.80 0.02 0.02 0.02 0.46 0.60 0.80 60.62 76.54 76.79 0.56 0.74 0.74 0.02 0.02 0.02 0.57 0.72 0.74 RF 56.42 80.24 86.17 0.51 0.78 0.84 0.02 0.02 0.01 0.50 0.74 0.85 bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 880 881 882 bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Table 3: Class accuracy metrices on best classified image i.e., Random Forest classifies the Integrated image. The class accuracy metrices evaluated by Omission rate, Commission rate, Producer’s accuracy, User’s accuracy and F – stat. Agriculture Sparse vegetation Barren Scrub Juniper patch Settlement Permafrost Water Road Producer's Accuracy (PA) LCLU Class Agriculture Sparse vegetation Barren Scrub Juniper patch Settlement Permafrost Water Road F- statistics LCLU Class 889 Agriculture Sparse vegetation Barren Scrub Juniper patch Settlement Permafrost Water Road LISSIV 47 Sentinel Integrated 2A 23 14 57 13 42 36 70 2 34 91 LISSIV 53 26 20 2 1 7 18 17 13 34 23 0 0 11 3 58 31 Sentinel Integrated 2A 77 86 43 87 58 64 30 98 66 9 LISSIV 53 74 80 98 99 93 82 83 87 66 77 100 100 89 97 42 69 Sentinel Integrated 2A 75 85 50 60 49 50 44 88 76 16 78 76 86 74 78 99 94 57 84 80 80 85 85 100 98 80 LCLU Class Commission Omission LCLU Class Agriculture Sparse vegetation Barren Scrub Juniper patch Settlement Permafrost Water Road LCLU Class User's accuracy (UA) 886 887 888 Agriculture Sparse vegetation Barren Scrub Juniper patch Settlement Permafrost Water Road LISSIV 47 Sentinel Integrated 2A 27 16 40 55 57 59 16 21 9 33 LISSIV 53 17 11 38 33 20 22 33 17 3 4 1 0 1 1 14 5 Sentinel Integrated 2A 73 84 60 45 43 41 84 79 91 67 83 62 80 67 97 99 99 86 89 67 78 83 96 100 99 95 Table 4: Evaluation metrics to evaluate the efficiency of the participating distribution models for Himalayan Ibex in the study landscape. Participating models are BRT (Boosted Regression Tree), GLM (Generalized Linear Model), MARS (Multivariate adaptive regression splines), MAXENT (Maximum Entropy Model), RF (Random Forest) and the efficiency of the models evaluated by AUC (area under the receiver operator curve), PCC (Proportion Correctly Classified), sensitivity, specificity, Cohen's kappa and TSS (True Skill Statistic). CV mean (Cross Validation) data used for model evaluation and train split used for the model, which was assessed by model evaluation. (a) LISS IV derived LCLU used for this model building, (b) Sentinel 2A derived LCLU used for this model building and (c) Integrated image derived LCLU used for this model building. 897 (a) Model AUC PCC Sensitivity Specificity Kappa TSS BRT 0.92 84.67 0.85 0.84 0.69 0.69 GLM 0.78 68.1 0.65 0.71 0.36 0.36 MARS 0.9 80.37 0.8 0.8 0.61 0.61 MAXENT 0.9 83.95 0.84 0.84 0.68 0.68 RF 0.77 70.55 0.7 0.71 0.41 0.41 898 899 (b) Model 900 901 (c) AUC PCC Sensitivity Specificity Kappa TSS BRT 0.91 82.32 0.8 0.84 0.65 0.65 GLM 0.81 74.39 0.74 0.74 0.49 0.49 MARS 0.82 75 0.74 0.76 0.5 0.5 MAXENT 0.91 85.28 0.85 0.85 0.71 0.71 RF 0.77 70.73 0.71 0.71 0.41 0.41 bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 890 891 892 893 894 895 896 902 0.4 0.4 0.71 0.69 0.77 RF 69.94 0.74 0.74 0.85 0.89 0.92 MAXENT 87.04 0.63 0.63 0.82 0.81 0.87 MARS 81.6 0.4 0.4 0.71 0.69 0.8 GLM 69.94 0.57 0.57 0.79 0.78 78.53 0.89 BRT Model AUC PCC Sensitivity Specificity Kappa TSS bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 903 904 905 906 Figure 1: Depiction of present study area along with the its location in India. bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 907 908 909 910 Figure 2: Classified images from LISS IV, Sentinel 2A and Integrated images by ML, GMM, KNN, SVM and RF classifier algorithms. bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 911 912 913 Figure 3: AUC plots of five algorithms on different sourced images to predict species distribution model. bioRxiv preprint doi: https://doi.org/10.1101/2023.07.18.549476; this version posted July 19, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 914 915 916 Figure 4: Probability (model agreement maps) maps and binary maps from three different sourced images generated LCLU to predict species distribution model of Himalayan Ibex. 917 918 919 920 Figure 5: Variance in suitable habitat prediction of Himalayan Ibex by three different sourced images generated LCLU.