Accuracy assessment of annual land cover time series derived from change-based updating

2013 ◽  
Author(s):  
Darren Pouliot ◽  
Rasim Latifovic
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Cover Time

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals A Forecasting Approach to Online Change Detection in Land Cover Time Series

2019 ◽  
Vol 12 (5) ◽  
pp. 1451-1460
Author(s):  
Willem C. Olding ◽  
Jan C. Olivier ◽  
Brian P. Salmon ◽  
Waldo Kleynhans
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Cover Time ◽  
Online Change Detection

scholarly journals Applying habitat and population‐density models to land‐cover time series to inform IUCN Red List assessments

2019 ◽  
Vol 33 (5) ◽  
pp. 1084-1093 ◽  
Author(s):  
Luca Santini ◽  
Stuart H. M. Butchart ◽  
Carlo Rondinini ◽  
Ana Benítez‐López ◽  
Jelle P. Hilbers ◽  
...  
Keyword(s):  
Time Series ◽  
Population Density ◽  
Land Cover ◽  
Iucn Red List ◽  
Red List ◽  
Cover Time

scholarly journals A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 – 2019) based on LUCAS, CORINE and GLAD Landsat

2021 ◽  
Author(s):  
Martijn Witjes ◽  
Leandro Parente ◽  
Chris J. van Diemen ◽  
Tomislav Hengl ◽  
Martin Landa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Data Science ◽  
Cross Validation ◽  
Series Data ◽  
Land Use Land Cover ◽  
Cover Time ◽  
Learning Framework

Abstract A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.

LBA-ECO CD-06 LAND USE/LAND COVER TIME SERIES, JI-PARANA BASIN, BRAZIL: 1986-2001

2007 ◽  
Author(s):  
M. V. R. BALLESTER ◽  
C. FERNANDES ◽  
L. HANADA ◽  
ALEX V. KRUSCHE ◽  
R. L. RICHEY ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Cover Time ◽  
Paraná Basin ◽  
Parana Basin

scholarly journals A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 – 2019) based on LUCAS, CORINE and GLAD Landsat

2021 ◽  
Author(s):  
Martijn Witjes ◽  
Leandro Parente ◽  
Chris J. van Diemen ◽  
Tomislav Hengl ◽  
Martin Landa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Land Cover ◽  
Data Science ◽  
Cross Validation ◽  
Series Data ◽  
Cover Time ◽  
Learning Framework ◽  
Tree Classifier

Abstract A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of long-term LULC dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including 5~million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and a artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 44 (level-3), 14 (level-2), and 5 classes (level-1). The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer. Functions used to prepare data and run modeling are available via the eumap library for python.

Mapping Annual Urban Change Using Time Series Landsat and NLCD

2019 ◽  
Vol 85 (10) ◽  
pp. 715-724 ◽  
Author(s):  
Heng Wan ◽  
Yang Shao ◽  
James B. Campbell ◽  
Xinwei Deng
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Break Point ◽  
Detection Methods ◽  
Urban Change ◽  
United States Geological Survey ◽  
Urban Intensification ◽  
Point Detection

Annual urban change information is important for an improved understanding of urban dynamics and continuous modeling of urban ecosystem processes. This study examined Landsat-derived Normalized Difference Vegetation Index (NDVI) time series for characterizing annual urban change. To reduce impacts from cloud contamination and missing data, United States Geological Survey (USGS) Landsat Analysis Ready Data were processed to derive annual NDVI layers using a maximum value composite algorithm. National Land Cover Database land cover products from 2001 and 2011 were used as references for generating a decadal urban change mask. Within the decadal urban change mask and using annual NDVI as input, we examined three time-series change detection methods to pinpoint specific year of urban change: (a) minimum-value method, (b) break-point detection, and (c) simple-threshold identification. For accuracy assessment, we divided change pixels into urbanization and urban-intensification pixel groups, defined by initial land cover types. We used Google Earth's High-Resolution Imagery Archive as primary reference data for detailed accuracy assessment. Overall, the urbanization pixel group has good change detection accuracies of above 82% for all three change detection algorithms. The break-point detection method resulted in the highest overall accuracy of 88%. Overall accuracies for urban intensification pixel group were in the range of 35%–76%, depending on choice of change detection algorithm, length of input time-series, and further division of pixel subgroups.

Sign in / Sign up

Export Citation Format

Share Document