Availability of green vegetation cover is always a desirable component of the terrestrial ecosystems. Increase in green vegetation cover indicates increase in primary productivity that is assumed to support higher animal species richness (Bailey et.al., 2004). This is even more important in the protected areas within the fragmented landscape that are viewed as the ultimate refuge for sustaining biological diversity amid widespread degradation of the natural areas (Connell & Orias, 1964). As biological diversity largely depends on the primary productivity of the terrestrial ecosystems, periodic monitoring of vegetation greenness is essential. Quantification of trends in vegetation productivity is also crucial for understanding the response of ecosystems to the environmental change (Liu et al., 2015), including influences on water resources, agriculture, biodiversity (Xu et al., 2017), and wildlife conservation (Prasai, 2021, Liu et al., 2015). Normalized Difference Vegetation Index (NDVI) is one of the most widely used vegetation indices ( Mlenga & Jordaan, 2020; Baniya et al., 2018;Wilson & Norman, 2018;) that is a reliable indicator of vegetation dynamics, and can be derived from satellite imagery (Liu et al., 2015; Tian et al., 2015; Galvão et al., 2011). This index has been extensively used to measure the biotic response to climate change (Tian et al., 2015), monitor drought (Thieme et al., 2020; Baniya et al., 2018; Sarma et al., 2015), understand forest carbon dynamics ( Liu et al., 2015), classify land use and land cover (LULC) and LULC change (Schut et al., 2015), and estimate land degradation (Galvão et al., 2011).
Nepal, a hotspot of endangered wildlife with an important eco-tourism industry (Kafley et al., 2019), is an example of a country where ecosystems appear to be rapidly changing due to climate change (Adhikari et.al, 2021; Marino et al., 2019; Baniya et al., 2018). One of the most impressive collections of wildlife species in the world reside in the Chitwan National Park (CNP) of Nepal (Stapp et.al., 2015). CNP, with the size of 952.63 km2, represents the largest contiguous protected area of the country, and was designated as the world heritage site in 1984 (Fig. 1). Researchers have estimated at least 700 different terrestrial vertebrate species in the park, including several endangered mammals such as the Bengal tiger (Panthera tigris), clouded leopard (Neofelis nebulosa) (Kafley et al., 2019), one-horned rhinoceros (Rhinoceros unicornis), sloth bear (Melursus ursinus), and Indian pangolin (Manis crassicaudata) (Plains & Park, 2019). Around 500 species of birds have been documented from the park including threatened and endangered bird species such as the swamp francolin (Francolinus gularis) and Bengal florican (Houbaropsis bengalensis) (Kafley et al., 2019). Assessing temporal trend of NDVI allows the understanding of patterns and processes of vegetation ecology (Dai et al., 2020). The existing and changing pattern of physical and biological environmental conditions can be determined for efficient environmental management (Krakaeur et al., 2017). Monitoring temporal trends of vegetation indices assist in understanding the ecosystem dynamics to assess whether the ecosystem is restoring or degrading Wasniewski et al., 2020; Moffiet et al., 2006).
NDVI is a ratio of difference between the red (R) and near infrared (NIR) intensities to their sum (i.e., (NIR - R) / (NIR + R)) and ranges from − 1 to + 1 (Rouse et al., 1973). Water takes values closer to -1, green vegetation takes values close to + 1 (Tong et al., 2019), and values close to 0 indicate urbanized area and lack of vegetation (Galvão et al., 2011). In many scenarios, values below 0.1 correspond to bodies of water and bare ground (Kim et al., 2017; Xu et al., 2017), while higher values are indicators of high photosynthetic activity linked to scrub land, temperate forest, rain forest and agricultural fields containing growing crops (Moura et al., 2012). Given the ability of NDVI to quantify vegetation greenness and density, it provides a useful tool for measuring plant health and productivity over time (Baniya et al., 2018; Xu et al., 2017; Tian et al., 2015; Tian et al., 2015), photosynthetic activity ( Wingate et al., 2019; Gillespie et al., 2018), plant phenology (Bai et al., 2019; Novillo et al., 2019; Y. Liu et al., 2015), trophic interactions (Luan et al., 2018), biomass (Galvão et al., 2011), and the global carbon cycle (Novillo et al., 2019). Given the relatively fine spatial resolution of 30 m and moderately high temporal resolution of 16 days of Landsat imagery (Xu et al., 2017), the generated NDVI have the potential to answer many questions that require high spatial and temporal details in the index.
Traditionally, accessing the Landsat data that requires a reliable source with easy-to-interact API, and deriving the NDVI over a large space and for a long time period is a computationally intensive task. Both of these complexities are addressed by Google Earth Engine (GEE), which is a cloud-computing platform (Prasai et.al, 2021; Gorelick et al., 2017). GEE utilizes Google’s computational infrastructure and open access remote sensing datasets (Coleman et al., 2020; Mutanga & Kumar, 2019). The GEE platform enables users and researchers to easily and quickly access freely available public data archives which can be used to develop global and large-scale remote sensing applications ( Xia et al., 2019; Midekisa et al., 2017). Historically, such analyses required that researchers find the appropriate source of remote sensing data, download and store them locally and run the computation algorithms in their local machine (Coleman et al., 2020). Analyses were limited by local storage, processing power, and the time required to download very large datasets (Prasai et al.,2021). With GEE, data from various sources are immediately available for analysis without having to download locally (Gorelick et al., 2017). GEE also provides free powerful Graphics Processing Unit (GPU) that can analyze raster data faster than local computers (Coleman et al., 2020). GEE has the features of an automatic parallel processing and fast computational platform to effectively deal with the challenges of big data processing (Coleman et al., 2020; Martin et al., 2019; Gorelick et al., 2017). In addition, users can study and explore their own dataset in the GEE platform (Coleman et al., 2020; Shaharum et al., 2020; Xia et al., 2019). GEE has been used in a number of research projects related to vegetation mapping and monitoring (Xia et al., 2019), land cover mapping (Hamunyela et al., 2020), crops mapping (Thieme et al., 2020), disaster management (Midekisa et al., 2017), earth sciences related research, and several similar studies (Xia et al., 2019). We used GEE python API to assess NDVI trends for the years 1988–2020 in Chitwan National Park (CNP), Nepal. The objectives of this study were to (1) assess the vegetation dynamics in CNP from 1988–2020 and (2) evaluate the utility of GEE Python API in deriving NDVI information. In this study, we studied annual and seasonal NDVI changes in CNP from 1988–2020 to identify its annual and seasonal variation. Although numerous studies on vegetation dynamics have been conducted in this region ( Dai et al.,2020; Baniya et al., 2018; Krakaeur et al., 2017), only limited studies have used remote sensing approach and none has used GEE, one of the newest methodological approaches.