Mapping Urbanization Trends in a Forested Landscape Using Google Earth Engine
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Land cover change is one of the most important issues facing the landscape of the Southeastern United States. Land use change impacts the natural environment, and it is critical to understand the location and rate of change in forests near rapidly urbanizing areas. The objectives of this study are to determine the classes and the distribution of land cover using classification of high-resolution satellite imagery for the upstate region of South Carolina and identify urbanization trends (conversion of forested areas to residential or industrial developments). Rapid urbanization has occurred in the Southeastern U.S. because of economic development and population growth. The challenge is to develop methodologies to quickly identify how the landscape is being altered as forested areas are developed. Remote sensing techniques using newly available high-resolution imagery have great potential for providing up-to-date spatial information about the land cover change. In this study, a framework has been developed to regularly monitor land cover change using a new geospatial technology platform: Google Earth Engine (GEE). The overall accuracy assessments of the 3 years were 91.21% (2013), 90.46% (2015), and 91.01% (2017), respectively. Based on the classification results, urbanization activities have resulted in a gradual change of land cover classes. The predominant land cover alteration at each time interval was changes from forested areas to the new development lands, bare lands, and non-forested areas.
KeywordsEcosystem High-resolution imagery Land cover Unsupervised classification Urban
Land use change detection is essential for better understanding the interactions and relationships between human activities and the environment over time. Land use change is a common and accelerating process due to rapid urbanization, forest conversion, and agricultural expansion [1, 2]. Land use change is the most remarkable indicator of human footprint, and it is considered the most important driver of loss of biodiversity and land degradation . The effect of the change in land use varies by region and geographical location. At a smaller spatial scale, it was observed that conversion of land from forested and agricultural uses to urban and suburban uses can degrade aquatic ecosystems in small streams in the upstate region of South Carolina, with the impacts being particularly destructive during the actual land conversion process [4, 5, 6].
Forest disturbance is a key process in the development of forest ecosystems, and advances in remote sensing and geospatial technology have greatly increased the ability to visualize and record this disturbance [6, 7, 8, 9]. Monitoring forest disturbance over time is crucial to better understand the history of land use. Cohen et al.  studied forest disturbance from 1985 to 2012 across the USA and reported that harvest areas were more evident in the more heavily forested regions where the national rates of disturbance ranged between 1.5 and 4.5% of forest area per year. Huang et al.  were able to detect most stand clearing disturbance events, including harvest, fire, and urban development in the Western U.S. using an automated algorithm called the vegetation change tracker (VCT) that uses Landsat time series data. Zurqani et al.  studied the loss and gain of land cover in the Savannah River basin, Southeastern U.S., using a historical Landsat imagery, and they revealed that the major land use change observed in the area was the deforestation and reforestation of forest areas during the entire study period.
South Carolina has limited open space protections and was ranked third among 50 states in related regulations, according to a Sierra Club report . Campbell et al.  pointed out that developed land in the upstate South Carolina grew from 222,745 acres to 576,336 acres between the years 1990 and 2000, and they anticipated these areas to grow to 1,523,667 acres by the year 2030 based on a GIS-based logistic regression model.
The use of remote sensing techniques for land use analysis in the urban areas has been examined in many studies [7, 11, 12, 13]. These studies indicate that using remote sensing data such as Landsat, SPOT, and National Agriculture Imagery Program (NAIP) imagery can help characterize the change in urbanization on a large scale. To improve urban forest planning and management at the fine or neighborhood scale, high spatial resolution imagery is appropriate to identify individual tree locations [14, 15]. Zhang  pointed out that the use of the high spatial resolution data for small regions can produce accurate land cover maps.
Traditional techniques of mapping and monitoring land cover/use change such as deforestation, urban growth, agricultural expansion, and wetland loss require downloading remote sensing imagery and having adequate CPU processing power to classify imagery. This is often expensive in terms of both financial cost and time, especially when large areas and/or long-time periods are analyzed. On December 2, 2010, Google launched a new technology named Google Earth Engine (GEE) (U.S. Geological Survey 2010) . This new geospatial analysis platform made much of the freely available satellite imagery available online so that researchers can analyze changes to the Earth’s surface in near real time [6, 17, 18]. Google Earth Engine allows users to run algorithms on a large archive of georeferenced images and other data within Google’s infrastructure. This study utilizes this new geospatial technology and the NAIP high-resolution imagery that are freely available in GEE to investigate the land use change within the urbanizing areas of Greenville County in upstate South Carolina, USA.
The objectives of the study were to: (1) determine the classes and the distribution of land cover using classification of high-resolution satellite imagery for the upstate region of South Carolina, (2) identify where forested areas have been converted to residential or industrial developments, and (3) discuss the potential impacts of land use change in this region.
2 Materials and Methods
2.1 Study Area
2.2 Data Processing
The change detection technique for identifying a change in land cover requires image preprocessing and normalization, a reference dataset, and land cover classification. The land use change detection technique was applied and evaluated by developing code in the GEE platform using the unsupervised clustering algorithm which was used to classify high-resolution NAIP aerial imagery individually for each study year. The NAIP imagery was also used to generate the reference dataset for validating the classifications, which is possible because of its high spatial resolution. Finally, the type of land use change detection was determined using a post-classification approach by overlying the land cover classification maps, and the change detection was described and identified using NDVI Change Ratio to Previous Year (RPNDVI) approach. The general procedures are summarized in the flowchart illustrated in (Fig. 3).
2.3 Image Preprocessing
Data sources and description
Aerial imagery (NAIP)
Google Earth Engine (GEE)
2013, 2015, and 2017
Study area boundary
Google Earth Engine (GEE)
2.4 Image Classification
A wide variety of classification algorithms have been used to map the changes in land cover/land use from remotely sensed data [3, 28]. The unsupervised classification has been shown effective in terms of classification accuracy and stronger noise resistance and is less time-consuming than other techniques [29, 30, 31]. For example, Sader et al.  obtained an overall accuracy of 80% for forest and wetland classifications in Maine using Landsat TM and GIS rule–based model with supervised classifications. An 8% improvement was obtained when the statistical clustering functions of unsupervised classifications were applied. Unsupervised classification is a classification of groups (groupings of pixels with common characteristics), and no extensive prior knowledge of the region is required. In this study, an unsupervised classification procedure was applied by employing a k-mean clustering algorithm in Google Earth Engine (GEE) platform (Appendix A in the Electronic Supplementary Material (ESM)). In this approach, pixel reflectance values are separated into clusters which represent the most general natural spectral groupings. The unsupervised classification technique is sensitive to the number of clusters, and therefore, the optimum number of clusters was determined to be 100 clusters after experimenting and visually evaluating a range of from 10 to 1000 clusters. The number of clusters for each of the land cover classes was selected based on visual assessment. Six land cover classes were identified across the study area using the National Land Cover Database (NLCD) maps and description , and the high-resolution imagery National Agriculture Imagery Program (NAIP) (1-m resolution) as a reference . The identified classes were: (1) open water with rivers, lakes, and standing water bodies; (2) low–medium-intensity urban with paved roads, concrete, and warehouses; (3) high-intensity urban with densely populated areas; (4) barren land; (5) forest which includes the deciduous and evergreen forests, and woody wetlands; (6) non-forest which includes the shrub/scrub, grassland/herbaceous, pasture/hay, and cultivated crops.
2.5 Accuracy Assessments
Many factors affect the accuracy of image classification. Accuracy assessments are useful and effective techniques to determine how well the classification process was accomplished [29, 33, 34]. The accuracy assessment process allows the analyst to compare certain pixel values in a raster layer to the reference pixels for which the class is known. The produced land cover classification maps were validated using high-resolution imagery (NAIP) (1-m resolution). A total of about 600 reference points were delineated using a random stratified sample design for the years 2013, 2015, and 2017, respectively, with no less than 50 reference points per land cover category. Following the previous studies [6, 29, 33, 34], a confusion matrix of land cover maps was calculated to evaluate the accuracy of the results using producer’s accuracy, user’s accuracy, the overall accuracy, kappa statistics , and F1 score which shows how good the classifier is in the context of both producer’s and user’s by weighting the average of producer’s and user’s [6, 36] as shown in Eq. (6):
2.6 Land Use Change Detection
The change detection approach was applied with a threshold value of negative (1.1) to the RPNDVI layer to detect the change between the years 2013 to 2015 and 2015 to 2017, and over the whole study period between the years 2013 to 2017. The noise of NDVI change ratio was filtered by grouping the pixels based on size and eliminating the small patches. This approach provides information that illustrates “from–to” change that has occurred which can be easily calculated and mapped . The pixels with change in this approach will be flagged as “probable change.”
3 Results and Discussion
3.1 Classes and the Distribution of Land Cover Using Classification of High-Resolution Satellite Imagery for the Upstate Region of South Carolina
The percentage of producer and user accuracy, F1 score, overall accuracy, and kappa statistic for land cover classification
Land cover classes
Distribution of land cover in 2013, 2015, and 2017
Land cover classes
Forested areas experienced the highest decline while the non-forested areas were increasing (Table 3). In the first study year (2013), the forested areas were 1402.52 km2 (66.33%), while the non-forested areas were 532.85 km2 (25.20%); in the second year (2015), forested areas declined to 1354.31 km2 (64.05%), while the non-forested areas increased to 543.76 km2 (25.71%). In the last year of the study period (2017), the forested areas decreased to 1326.93 km2 (62.75%), while the non-forested increased to 553.19 km2 (26.16%), respectively. Water bodies showed little change over the study period where it was 21.35 km2 or 1.01% in the first year (2013) and 20.79 km2 or 0.98% in the second year (2015). However, the water areas declined slightly to 19.45 km2 (0.92% of the study area) over the whole study period (2017).
3.2 Land Use Change Detection Using Post-Classification Comparison Approach
For the whole study period, the predominant land cover alteration at each time interval was generally caused by changes from forested areas to the new development lands, bare lands, and non-forested areas. The association plot in Fig. 5 indicates that loss of the forested areas is greater than would be expected especially during the whole study period especially during the relatively short time interval between 2013 and 2017. While the non-forested areas showed little overall gain and loss during the whole study period, the urban areas show gains over the whole study period which was clearly evident between 2013 and 2017. Bare lands show greater than would be expected gains in the first and the second intervals (2013–2015 and 2015–2017), while these areas show an unexpected loss during the whole study period between 2013 and 2017. The water bodies showed little loss and gain in all time intervals.
3.3 Land Use Change Detection Using NDVI Change Ratio
Land use change areas from 2013 to 2015, 2015 to 2017, and the overall change from 2013 to 2017
Land use change
Land use change
Land use change
Land use change
3.4 Potential Impacts of Land Use Change
Understanding the land use change and its potential impact is key to the future success of increasing environmental awareness and monitoring . Producing accurate maps of land cover over time can provide important information for a better understanding of urban ecosystems and help improve environmental quality and human health in urban areas. Unlike traditional GIS software, utilizing Google Earth Engine makes it much easier to integrate remote sensing data as well as the image normalization process to improve the classification accuracy . In this study, the NAIP imagery and its normalization results have a significant contribution to decreasing the confusion between diverse types of land cover (Figs. 2 and 4).
New residential development areas in the Southeastern United States peaked in early 2006, but since then declined to leave many residential developments stalled in various stages of construction . Greenville County is the most populous county in South Carolina, and it continues growing up at an average rate of 2.1% per year (https://www.greenvillecounty.org/). Comparison of the land cover classification results over the entire study period indicates a clear increase in the urban areas coupled with a decrease in the forested area (Table 3). Changes in the forested area were illustrated as where the deforestation and the urbanization activities occur using the PNDVI-revealed evidence supporting the case of this study. The accelerated development of urban areas has replaced natural vegetation with impervious surfaces such as buildings, paved roads, and parking lots . The impervious surfaces are associated with various anthropogenic and economic activities generating pollutants, such as the discharge of residential and industrial sewage . In addition, a higher percentage of impervious surfaces prevent rainfall infiltration into the soil, leading to contaminating the nearby streams by the soluble and particulate forms of pollutants through surface runoff . These paved areas affect the temperature of stormwater runoff and increase the environmental hazard in the area especially on the flooding season . Forested areas typically experience little to no management and largely need to have constant attention over time. In future urban development, urban planners should be aware of the adverse impacts of impervious surfaces.
The accessibility of historical remotely sensed data as well as the new geospatial technology of Google Earth Engine represents a significant improvement for monitoring and evaluating land use change over time. This study successfully developed a regional scale analysis using high-resolution imagery, determine the classes and the distribution of land cover in Greenville, SC, and identified the spatial and the temporal changes of the land cover types that occurred as a consequence of land use change over time. Multiple layers were used including the original four bands RGB and NIR, NDVI, EVI, GRVI, MSAVI, and NDWI which provided reliable results in classifying six general land cover types.
Classifying high spatial resolution aerial imagery using remote sensing analysis is an efficient way to get accurate land cover maps of individual cities. This study successfully produced a general land cover classification maps for the city of Greenville, SC, for the years 2013, 2015, and 2017. The overall accuracy of the 3 years exceeded 90% for the six classes of high-intensity urban, low–medium-intensity urban, forested areas, non-forested areas, bare soil, and water. The potential impact of the land use change took place near the center of the Greenville County, and most of the direct impact on the study area appeared to be clearing of the trees and the establishment of new development areas.
This methodology is available at no cost to the users, and the advantage of this proposed approach can be useful for describing detailed land cover types within the locations of different areas of the city. This type of information facilitates the research and management of the urban ecosystem to improve environmental quality and human health in the area. The limitation of this approach is that the NAIP imagery is not yearly available at some locations. In future research, continuous monitoring of land use change is needed to better understand its impact in the region and which returns more effective management strategies.
Financial support for this project was provided by Libyan Government (Ministry of Higher Education and Scientific Research) on behalf of the University of Tripoli and Clemson University.
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