A Cloud-Based Evaluation of the National Land Cover Database to Support New Mexico’s Food–Energy–Water Systems

Melakeneh G. Gedefaw,Hatim M.E. Geli,Kenneth G. Boykin,Ashraf J. Zaied,Kamini Yadav,Yelena Finegold

doi:10.3390/rs12111830

Abstract

Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales.

Highlights

Natural ecosystems have pivotal roles in providing water, energy, land, and food for humans’ societal and economic development [1,2]
The main goal of the study was to examine the usability of the single-date National Land Cover Database (NLCD) legacy maps (i.e., 1992, 2001, 2006, 2011, and 2016) at the county level decisions to help in resource monitoring, management, and conservation in New Mexico as well as other areas with similar semi-arid conditions
This research highlighted some of the uncertainties related with the use of NLCD area estimates over a few LULC classes that mostly contribute to New Mexico (NM’s) Food–Energy–Water Systems (FEWS) in six counties that include San Juan, Curry, Roosevelt, Lea, Eddy, and Doña Ana

Summary

Introduction

Natural ecosystems have pivotal roles in providing water, energy, land, and food for humans’ societal and economic development [1,2]. A recent special report by the Intergovernmental Panel on Climate Change (IPCC) on climate change and land indicated that humans modify more than 70% of the ice-free land surface [7] In this regard, land use activities, including those for food production, which use 1/4 to 1/3 of global land’s potential primary production and the shifts in land management, constitute the main factors that change and transform significant proportions of the Earth’s environment and directly contribute to climate change [1,7]. Understanding the role of these natural ecosystems spatially and temporally is important to assess their ability to continue providing sustainable services to humans’ wellbeing and the environment [2] Their robust mapping, assessment, and development of spatially descriptive information for monitoring and policy implementation purposes are critical [10]

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