Reply on RC2

George Xian

doi:10.5194/essd-2021-202-ac2

Abstract

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Highlights

Changes in land cover and land surface are one of the greatest and most immediate influences on the Earth system, and these changes will continue in association with a surging human population and growing demand on land resources (Szantoi et al, 2020)
We describe our novel approach to implement the Change Detection and Classification (CCDC) algorithm to produce the LCMAP product suite composed of five land cover products and five products related to land surface change
We focused on how LCMAP employed every observation in a time series of US Landsat Analysis Ready Data (ARD) (Dwyer et al, 2018) over a long period starting with the 1980s to determine whether change occurred at any given point in the observation record

Summary

Introduction

Changes in land cover and land surface are one of the greatest and most immediate influences on the Earth system, and these changes will continue in association with a surging human population and growing demand on land resources (Szantoi et al, 2020). An improved understanding of the complex and dynamic interactions between the various Earth system components, including humans and their activities, is critical for policymakers and scientists (Foley et al, 2005, 2011) To fully understand these processes and monitor these changes, accurate and frequently updated land cover information is essential for scientific research and to assist decision makers in responding to the challenges associated with competing land demands and land surface change. Other national-scale mapping projects focus on specific land cover themes Among these are the Landscape Fire and Resource Management Planning Tools (LANDFIRE) (Picotte et al, 2019), which maps vegetation and fuels in support of wildfire management, and the Cropland Data Layer (Boryan et al, 2011) generated by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). Due to the need to incorporate data from neighboring years, as well as extensive post-processing, ancillary dataset dependencies, and

Methods

Results

Conclusion