Land change monitoring, assessment, and projection

North American Land-Change Monitoring System

Remote Sensing of Land Use and Land Cover ◽

10.1201/b11964-26 ◽

2016 ◽

pp. 324-345 ◽

Cited By ~ 1

Keyword(s):

Monitoring System ◽

North American ◽

Land Change ◽

Change Monitoring

Download Full-text

Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111356 ◽

2020 ◽

Vol 238 ◽

pp. 111356 ◽

Cited By ~ 13

Author(s):

Jesslyn F. Brown ◽

Heather J. Tollerud ◽

Christopher P. Barber ◽

Qiang Zhou ◽

John L. Dwyer ◽

...

Keyword(s):

United States ◽

Lessons Learned ◽

Land Change ◽

Change Monitoring

Download Full-text

Training Data Selection for Annual Land Cover Classification for the Land Change Monitoring, Assessment, and Projection (LCMAP) Initiative

Remote Sensing ◽

10.3390/rs12040699 ◽

2020 ◽

Vol 12 (4) ◽

pp. 699 ◽

Cited By ~ 4

Author(s):

Qiang Zhou ◽

Heather Tollerud ◽

Christopher Barber ◽

Kelcy Smith ◽

Daniel Zelenak

Keyword(s):

Land Cover ◽

Land Surface ◽

Classification Accuracy ◽

Training Data ◽

Land Change ◽

Auxiliary Data ◽

Surface Change ◽

Land Cover Map ◽

The U.S ◽

Change Monitoring

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative involves detecting changes in land cover, use, and condition with the goal of producing land change information to improve the understanding of the Earth system and provide insights on the impacts of land surface change on society. The change detection method ingests all available high-quality data from the Landsat archive in a time series approach to identify the timing and location of land surface change. Annual thematic land cover maps are then produced by classifying time series models. In this paper, we describe the optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). The results were evaluated and validated based on independent data from the training dataset. We found that refining the auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability. We improved the classification accuracy and stability considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class within geographic windows consisting of nine Analysis Ready Data tiles (450 by 450 km2). Comparisons revealed that the NLCD 2001 training data delivered the best classification accuracy. Compared to the original LCMAP classification strategy used for early evaluation (e.g., Trends training data, 20,000 samples), the optimized classification strategy improved the annual land cover map accuracy by an average of 10%.

Download Full-text