Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine

Abstract Climate and land use/cover change are among the most pervasive issues facing the Southeastern United States, including the Savannah River basin in South Carolina and Georgia. Land use directly affects the natural environment across the Savannah River basin and it is important to analyze these impacts. The objectives of this study are to: 1) determine the classes and the distribution of land cover in the Savannah River basin; 2) identify the spatial and the temporal change of the land cover that occurs as a consequence of land use change in the area; and 3) discuss the potential effects of land use change in the Savannah River basin. The land cover maps were produced using random forest supervised classification at four time periods for a total of thirteen common land cover classes with overall accuracy assessments of 79.18% (1999), 79.41% (2005), 76.04% (2009), and 76.11% (2015). The major land use change observed was due to the deforestation and reforestation of forest areas during the entire study period. The change detection results using the normalized difference vegetation index (NDVI) indicated that the proportion areas of the deforestation were 5.93% (1999–2005), 4.63% (2005–2009), and 3.76% (2009–2015), while the proportion areas of the reforestation were 1.57% (1999–2005), 0.44% (2005–2009), and 1.53% (2009–2015). These results not only indicate land use change, but also demonstrate the advantage of utilizing Google Earth Engine and the public archive database in its platform to track and monitor this change over time.

[1]  Y. Setiawan,et al.  Land Use/Land Cover Change Detection in an Urban Watershed: A Case Study of Upper Citarum Watershed, West Java Province, Indonesia☆ , 2016 .

[2]  Jerry Melillo,et al.  Effect of land-cover change on terrestrial carbon dynamics in the southern United States. , 2006, Journal of environmental quality.

[3]  C. Post,et al.  Does current management of storm water runoff adequately protect water resources in developing catchments? , 2008, Journal of Soil and Water Conservation.

[4]  Michael Schmidt,et al.  A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics , 2016, Remote. Sens..

[5]  Stuart R. Phinn,et al.  Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data , 2016, Remote. Sens..

[6]  Narumasa Tsutsumida,et al.  Measures of spatio-temporal accuracy for time series land cover data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Wei You,et al.  Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine , 2016, Remote. Sens..

[8]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[9]  Peter Selsam,et al.  Land cover changes assessment using object‐based image analysis in the Binah River watershed (Togo and Benin) , 2015 .

[10]  C. Tucker,et al.  Tropical Deforestation and Habitat Fragmentation in the Amazon: Satellite Data from 1978 to 1988 , 1993, Science.

[11]  E. Merem,et al.  Geospatial Information Systems Analysis of Regional Environmental Change along the Savannah River Basin of Georgia , 2008, International journal of environmental research and public health.

[12]  Land Use Changes: Economic, Social, and Environmental Impacts , 2008 .

[13]  C. Lo,et al.  Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area , 2002 .

[14]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[15]  Nina Siu-Ngan Lam,et al.  Methodologies for Mapping Land Cover/Land Use and its Change , 2008 .

[16]  M. Nash,et al.  Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin , 2011 .

[17]  Thomas R. Loveland,et al.  Land-use Pressure and a Transition to Forest-cover Loss in the Eastern United States , 2010 .

[18]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[19]  J. Wickham,et al.  Accuracy assessment of NLCD 2006 land cover and impervious surface , 2013 .

[20]  E. Crist A TM Tasseled Cap equivalent transformation for reflectance factor data , 1985 .

[21]  Ali Selamat,et al.  Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery , 2014, Remote. Sens..

[22]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[23]  Marian Vittek,et al.  Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990 , 2014, Remote. Sens..

[24]  Stuart R. Phinn,et al.  Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine , 2015 .

[25]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[26]  Tanju Karanfil,et al.  Impacts of Land Disturbance on Aquatic Ecosystem Health: Quantifying the Cascade of Events , 2008, Integrated environmental assessment and management.

[27]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[28]  Gilberto Câmara,et al.  Big earth observation data analytics: matching requirements to system architectures , 2016, BigSpatial '16.

[29]  Peng Gong,et al.  Mapping Urban Land Use by Using Landsat Images and Open Social Data , 2016, Remote. Sens..

[30]  Qingxi Tong,et al.  Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .

[31]  Hanqiu Xu Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematicoriented Index Combination Technique , 2007 .

[32]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[33]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[34]  G. Sun,et al.  Forest Management Challenges for Sustaining Water Resources in the Anthropocene , 2016 .

[35]  Matthew C. Hansen,et al.  Mapping wetlands in Indonesia using Landsat and PALSAR data-sets and derived topographical indices , 2014, Geo spatial Inf. Sci..

[36]  Toby N. Carlson,et al.  ANALYSIS AND PREDICTION OF SURFACE RUNOFF IN AN URBANIZING WATERSHED USING SATELLITE IMAGERY 1 , 2004 .

[37]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[38]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[39]  M. Bauer,et al.  Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing , 2005 .

[40]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[41]  Rafael Muñoz-Carpena,et al.  Wetland Landscape Spatio-Temporal Degradation Dynamics Using the New Google Earth Engine Cloud-Based Platform: Opportunities for Non-Specialists in Remote Sensing , 2016 .

[42]  Neelam Aziz,et al.  Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan , 2015 .

[43]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[44]  Dorothea Deus,et al.  Integration of ALOS PALSAR and Landsat Data for Land Cover and Forest Mapping in Northern Tanzania , 2016 .

[45]  Björn Waske,et al.  Classifier ensembles for land cover mapping using multitemporal SAR imagery , 2009 .

[46]  S. Sader,et al.  Accuracy of landsat-TM and GIS rule-based methods for forest wetland classification in Maine , 1995 .

[47]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[48]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[49]  Richard N. Palmer,et al.  Water Resources Implications of Global Warming: A U.S. Regional Perspective , 1999 .

[50]  Alice G. Laborte,et al.  Spectral Signature Generalization and Expansion Can Improve the Accuracy of Satellite Image Classification , 2010, PloS one.

[51]  Yeqiao Wang,et al.  Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects , 2009 .

[52]  Ling Zhang,et al.  Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China , 2016, PloS one.

[53]  Annemarie Schneider,et al.  Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach , 2012 .

[54]  Heiko Balzter,et al.  Methods to Quantify Regional Differences in Land Cover Change , 2016, Remote. Sens..

[55]  Jie Tian,et al.  Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China , 2016, Remote. Sens..

[56]  T. Brown,et al.  Spatial Distribution of Water Supply in the Coterminous United States 1 , 2008 .

[57]  E. Merem,et al.  Analyzing Environmental Issues in the Lower Savannah Watershed, in Georgia and South Carolina , 2015 .

[58]  Bruce W. Pengra,et al.  Distribution and dynamics of mangrove forests of South Asia. , 2015, Journal of environmental management.

[59]  J. Smink,et al.  Evaluating the collective performance of best management practices in catchments undergoing active land development , 2008, Journal of Soil and Water Conservation.

[60]  John W. Jones,et al.  Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison within situ Data from the Everglades Depth Estimation Network , 2015, Remote. Sens..

[61]  C. W. Cooke,et al.  Geology of the Coastal Plain of South Carolina , 1936 .

[62]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.