Urban land use change is a significant process resulting from global environmental dynamics (Hersperger et al., 2018; Lasode et al., 2021; Verburg et al., 2015). The formation and operation of an ecosystem is greatly affected by urban land uses (Oluseyi, 2006). The challenges associated with urban land use change include overcrowding, crime, pollution, increased energy utilization, and a loss of viable farming land (Oluseyi, 2006; Seto & Kaufmann, 2003). Remote sensing aids the precise procurement of urban land-use information for developing urban districts; however, inadequate remote sensing information limits the acquisition of urban land use data (Herold et al., 2002). Remote sensing can also be used in explaining the spatial distribution between various land uses in urban districts (Rinner & Hussain, 2011; Yao et al., 2017).

New Braunfels (Figure 1) is a city that falls inside Comal and Guadalupe counties in Texas, USA. The city spans across 44.9 square miles (116 km²) and had a total population of about 90,209 people in 2019 (Lopez, 2018). New Braunfels is considered a suburb just north of San Antonio, and part of the Greater San Antonio metropolitan area (Rogers & Sletto, 2010). New Braunfels is one of the cities in the US experiencing a massive population influx. It was ranked by the US Census Bureau, the second fastest growing city in the US between July 1, 2016 and July 1, 2017, having an 8% increase and a total population of 79,152 people (Census Bureau, 2016). This population growth is projected to continue. The city is located in the southeastern part of Comal County, about 48 miles (77 kilometers) southwest of Austin, right between the cities of San Marcos and San Antonio with the Interstate highway 35 (IH-35) cutting through it.

New Braunfels has experienced a 158% population growth since the year 1990. New Braunfels has a median age of 37 years and a considerable amount of its land use consists of residential areas (Census Bureau, 2016). Specifically, low density residential land uses make up a large part of the city, alongside commercial, industrial and open space. Commercial land uses occur along IH-35, with

some pockets of commercial and industrial areas along the parkways of Loop 337, State Highway 46 and FM 306. Waterways together with open space blend together with residential and commercial spaces. Other mixed land use types are seen around Gruene and Creekside areas in New Braunfels. Schools, institutional and government uses are located throughout the city.

This study performs a change detection analysis to uncover those changes in land use that have occurred in the city of New Braunfels, Texas in the last seven years (2013-2020). In this study, we examine those parts of New Braunfels that have had considerable changes in land usage. The specific objectives of this study are as follows:

1) Construct a Normalized Vegetation Index (NDVI) for images from 2013 and 2020 and compare their indices.

2) Classify both images (2013 and 2020) using a supervised classification algorithm into 4 classes namely forest, water, grassland and built-up.

3) Conduct an accuracy assessment to assess classification results.

4) Post-classify the classified images to observe for land cover changes in both years (2013 and 2020)

For this study, the dates of August 31, 2013 and November 5, 2020 were chosen. These dates were specifically selected based on availability of data having significantly less cloud cover (0.13% and 0.19% cloud cover respectively). A relatively high resolution (30 meters) Landsat 8 Imagery for both dates was derived from the United States Geological Survey (USGS) Earth Explorer database (https://earthexplorer.usgs.gov/). The images were already georectified and aligned with the city of New Braunfels boundary shapefile which was sourced from the city of New Braunfels Geographic Information Systems (GIS) department. The New Braunfels city boundary Shapefile was used to clip both Landsat images using ArcGIS Pro version 2.6, a GIS software application. Microsoft EXCEL was used to construct a bar chart to graphically show the observed changes in land use classes in both years.

A land use classification scheme was used to group land and water features in the images into distinctive land use classes. For this, a subset of the pre-defined and widely used 2016 National Land Cover Dataset (NLCD) classification (Jin et al., 2019) was used. This land use classification scheme was modified from the Anderson Land Cover Classification System (Anderson, 1976). The shortlisted classes used in this study are listed in Table 1 below.

This study examined the city of New Braunfels, Texas, USA to assess for any observable land cover/land use changes from August 31, 2013 to November 5, 2020. From the results, we noticed a considerable change in pixels in the built-up class (with an increase of 36,486 pixels from time 1 to time 2) followed by the grassland class which was a decrease of 29,414 pixels from time 1 to time 2. Forest class also had a decrease of 7387 pixels from time 1 to time 2 and Water class had the least gain of only 315 pixels more than time 1.

From the generic land cover change chart on Figure 5, we see again how the built-up class changed massively in the last seven years. This considerable change comes mainly as a result of the developments of residential and other urban land uses in New Braunfels. This also comes as a result of the continuous influx of people into the city which has created the need to clear out forests and possibly, cultivated lands in order to construct new apartment complexes and other urban structures. The NDVI change from time 1 to time 2 can also be attributed to the extent of change in the built-up class especially surrounding the main highways in the city (IH-35 and highway 46). Most of these parts of the city were covered with dense and healthy vegetation in time 1 but had been cleared out in time 2.

In the accuracy assessment process, we used 60 and 80 points (2 scenarios) distributed around the 4 land cover classes using both stratified random and random sampling methods. However, we observed, after 3 trials, that the water land cover class had got only a few points compared to the 3 other classes (grassland, built-up and forest) on both images. This may have produced a fairly biased result and impacted on both the users and producer's accuracy values. This limitation was in part due to the water class having a limited number of pixels (only a very small part in both images had water). Perhaps, it would be helpful if one could manually place the reference points on the images instead of depending on the randomly generated points but again, this could aggravate user bias. Another solution to this could be to increase the number of randomly generated points. Another limitation to this study was that of the spatial resolution of the images used in this study. Perhaps, higher spatial resolution images could have impacted on the producers, users and overall accuracy assessment (Reba & Seto, 2020; Zhou et al., 2018).

Results from this study can aid the appropriate bodies in planning properly for the city of New Braunfels and other cities as regards resource allocation and distribution. As noted by (Dong et al., 2020) in their land use land cover change study of Changchun, most cities place their emphasis on economic development which often lead to increased pressure on urban land, as such, urban planners should explore strategies to promote urban sustainable development. Cities can also apply land use change methods in determining areas where growth and development need to be promoted as well as where they need to be controlled.

All authors of this work performed substantial contributions to conception, design, analysis and writing and design of this article. All authors have read the manuscript for important intellectual content and approved the final version for publication.

The authors declare no conflicts of interest regarding the publication of this paper.

Lasode, M. K., Esobi, I. C., Anyanwu, C. I., & Lasode, D. O. (2020). Assessing Urban Land Use Change in New Braunfels, Texas from 2013 to 2020. Journal of Geoscience and Environment Protection, 8, 232-243. https://doi.org/10.4236/gep.2020.812014