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Mapping Vegetation Species Succession in a Mountainous Grassland Ecosystem Using Landsat and Sentinel-2 Data

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Mapping Vegetation Species Succession in a Mountainous Grassland Ecosystem Using Landsat and Sentinel-2 Data bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Mapping Vegetation Species Succession in a Mountainous Grassland ecosystem 2 using Landsat and Sentinel-2 data 3 Adagbasa G. E1¶*, Mukwada G1,2,3 4 5 1Department of Geography, University of the Free State, South Africa 6 2Afromontane Research Unit, University of the Free State, South Africa 7 3Department of Geography & W.A. Franke College of Forestry & Conservation of the University of 8 Montana, USA 9 10 11 Corresponding Author: 12 E-mail address: [email protected] (EG) 13 14 15 16 17 18 19 20 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 21 Abstract 22 Vegetation species succession and composition are significant factors determining the rate of 23 ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and 24 effective natural resource management and biodiversity. The succession and composition of 25 grasslands ecosystems worldwide have significantly been affected by the accelerated changes in 26 the environment due to natural and anthropogenic activities. Therefore, understanding spatial data 27 on the succession of grassland vegetation species and communities through mapping and 28 monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This 29 study used a random forest machine learning classifier on the Google Earth Engine platform to 30 classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) 31 data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation 32 species succession. The results indicate that ASTER IM has the least accuracy of 72%, Landsat 7 33 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also show that other species had 34 replaced four dominant grass species totaling an area of about 49 km2 throughout the study. 35 Keywords: Grassland, Succession, Mountains, Remote sensing 36 37 1. Introduction 38 Vegetation succession has many economically significant attributes, including high overall 39 biomass and productivity, a wider variety of species, and minimal nutrients or energy from the 40 ecosystem (1). Nevertheless, an ecosystem with naturally occurring succession stages will be more 41 resilient to natural and anthropogenic disturbances, suppose these disturbances increase in severity, 42 frequency, and magnitude because of human activities and weather conditions. In that case, the bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 43 pressure on plant communities increases, thereby causing an accelerated succession and creating 44 a new vegetation community and allowing the succession of non-native species (2). 45 Luken (1) describes vegetation succession as a change in vegetation composition over 500 years 46 without been disturbed to achieve a stable species composition called a climax. Climate change 47 and other disturbances within a short period may result in fluctuations of species composition, 48 promote non-native species and delay the natural vegetational succession from reaching its climax. 49 The succession of non-native species could impact biodiversity and the natural ecosystem. Non- 50 native invasive species quickly inhabit disturbed spaces and delay native species from achieving 51 seral or climax states. In some cases, the succession is entirely taken over and held for an extended 52 period at an intermediate state, affecting biodiversity (3-5). Invasive vegetation species threaten 53 native vegetation species and water resources because they grow faster, consume more water, and 54 spread more than the native species (6, 7). The encroachment of these vegetation species tends to 55 alter the balance of ecosystems, thereby accelerating succession. Vegetation species succession 56 and composition are significant factors determining the rate of ecosystem biodiversity recovery 57 after being disturbed (8) and subsequently vital for sustainable and effective natural resource 58 management and biodiversity. For example, in South Africa, changes in vegetation succession 59 resulting from disturbances have led to significant losses in biodiversity (9). 60 Worldwide, the succession and composition of grasslands ecosystems have been significantly 61 affected by the accelerated changes in the environment due to natural and anthropogenic activities 62 (10, 11). It has resulted in shortages in grasslands taxonomy and efficient functioning of ecosystem 63 services (12). Grasslands' changing diversity and composition impact ecosystem services like 64 precipitation and temperature controls, freshwater supply, erosion control, and soil formation (13- 65 15). They can likely result in biodiversity loss (16). About one-third of South African land surface bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 66 is covered by the grassland biome (17). It has just less than 3% located in protected areas, and 40- 67 60% have been altered with little chance of been salvaged and returned. It makes the grassland one 68 of the most vulnerable biomes in South Africa (18). Therefore, understanding spatial data on the 69 succession of grassland vegetation species and communities through mapping and monitoring is 70 essential to gain knowledge on the ecosystem and other ecosystem services (9, 19, 20). 71 Remote sensing provides an efficient approach for mapping grassland vegetation species by 72 reducing rigorous fieldwork necessitated by standard mapping methods. It does this effectively by 73 offering a wide range of recent data on vegetation species distribution from hyperspectral and 74 multispectral imagery (21, 22). Extensive studies have been undertaken in monitoring spatio- 75 temporal changes in vegetation species composition and diversity using remote sensing data (23- 76 26). However, these studies focus briefly on a short period, usually between one to five years, 77 because, before now, only high-resolution hyperspectral images could give accurate vegetation 78 species discrimination at individual levels (9, 19, 27-30). Nevertheless, recent studies have shown 79 that free low-resolution satellite images like Sentinel-2 MSI and Landsat 8 OLI can be used to 80 accurately map and monitor grassland vegetation species (31-33). Vegetation species succession 81 and diversity monitoring can be done over an extended period using these low-resolution imageries 82 in combination with machine learning. Therefore, this study used Landsat 7 ETM+ and ASTER 83 MI data fused with the current Sentinel-2 MSI data to map and estimate the changes in vegetation 84 species succession. 85 2. Study Area 86 The study was conducted at the Golden Gate Highlands National Park, located in Free State, South 87 Africa, near the Lesotho border (Figure 1). The Park covers 340 km2 and is located at the foothills 88 of the Maloti Mountains of the Eastern Free State. The highest peak in the park is 2,829 m (9,281 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 89 ft) above sea level. The Park is positioned in the Eastern Highveld region of South Africa and 90 experiences a dry, sunny climate from June to August with showers, hail, and thunderstorms 91 between October and April and snow in winter. The Park has a relatively high annual rainfall of 92 800 mm (31 in). The park contains over 60 grass species (34). 93 94 95 96 97 98 99 100 101 102 Figure 1: The Study Area 103 104 105 106 3. Materials and Methods 107 The study used satellite images from different sensors to cover the period of study. The sensors 108 used include the European Union/ESA/Copernicus Sentinel-2 MSI, the United States Geological 109 Survey (USGS) Landsat series ETM+, and the Advanced Spaceborne Thermal Emission and 110 Reflection Radiometer (ASTER) multispectral imager satellite images with UTM Projection 111 Zone35S, and Datum WGS84 available on the Google earth engine (GEE). The imageries were 112 selected for the rainy months from November to April. The year 2001 was chosen for the Landsat 113 +ETM, 2011 for the ASTER MI, and 2021 for Sentinel-2. Landsat ETM+ and OLI were selected bioRxiv preprint doi: https://doi.org/10.1101/2021.08.18.456865; this version posted August 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 114 for this study because they have a 15m panchromatic band used to pansharpening the images from 115 30 m to 15 m.
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