Resources

Google Earth Engine

Introduction to Google Earth Engine (GEE)

SCO Example Code

Central America outputs for LandTrendr

ARSET Trainings

Using Google Earth Engine for Land Monitoring Applications  

Forest Mapping and Monitoring with SAR Data 

Change Detection for Land Cover Mapping

Land Cover Classification with Satellite Imagery

Remote Sensing of Forest Cover and Change Assessment for Carbon Monitoring

Remote Sensing for Monitoring Land Degradation and Sustainable Cities SDGs

Using Earth Observations for Pre- and Post-Fire Monitoring

Accuracy Assessment of a Land Cover Classification

Fundamentals of Remote Sensing

LandTrendr - related papers:

Cohen, W. B., Yang, Z., & Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync-Tools for calibration and validation. Remote Sensing of Environment, 114(12), 2911-2924.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.

Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), 2897-2910.

Kennedy, R. E., Yang, Z., Cohen, W. B., Pfaff, E., Braaten, J., & Nelson, P. (2012). Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment, 122, 117-133.

Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10, 691.

Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., & Egorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57-70.

LandTrendr vs. CCDC

Pasquarella, V., Arévalo, P., Bratley, K.H., Bullock, E.L. Gorelick, N., Zhiqiang, Y. & Kennedy, R.E. (2022). Demystifying LandTrendr and CCDC temporal segmentation, International Journal of Applied Earth Observation and Geoinformation, 102806,1569-8432.

CCDC-related papers (theory + applications)

Zhu, Z. & Woodcock, C.E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152-171.

Arévalo, P., Bullock, E.L., Woodcock, C.E. & Olofsson, P. (2020). A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine. Frontiers in Climate, 2:576740.

Chen, S., Woodcock, C.E., Bullock, E.L., Arévalo, P., Torchinava, P., Peng, S. & Olofsson, P. (2021). Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, 112648.

Chen, S., Olofsson, P., Sapangthong, T., &  Woodcock, C.E. (2023). Monitoring shifting cultivation in Laos with Landsat time series. Remote Sensing of Environment, 288, 113507.

Arévalo, P., Woodcock, C.E. & Olofsson, P. (2020). Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sensing of Environment, 238, 111051.

Tang, X., Hutyra, L. R., Arévalo, P., Baccini, A., Woodcock, C.E. & Olofsson, P. (2020). Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model. Science of The Total Environment. 720, 137409. 

Zhang, Y., Woodcock, C.E., Chen, S., Wang, J.A., Sulla-Menashe, D., Zuo, Z., Olofsson, P., Wang, Y. & Friedl, M.A. (2022). Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of Landsat data. Remote Sensing of Environment, 272,112935.

Carbon Emissions: 

Guatemala's REDD+ FREL Report

https://redd.unfccc.int/files/niveles_referencia_emisiones_forestales_guatemala_070222.pdf

Emission Estimation Guidelines

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories - https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html

REDD+ Sourcebook (COP 22 version 1) - http://www.gofcgold.wur.nl/redd/sourcebook/GOFC-GOLD_Sourcebook.pdf

Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests -https://www.reddcompass.org/documents/184/0/GFOI-MGD-3.0-en.pdf

Aboveground Woody Biomass Product Validation - Good Practices Protocol (Version 1.0) - https://lpvs.gsfc.nasa.gov/PDF/CEOS_WGCV_LPV_Biomass_Protocol_2021_V1.0.pdf

Biomass

Arévalo, P., Baccini, A., Woodcock, C.E., Olofsson, P. & Walker, W. S. (2023). Continuous mapping of aboveground biomass using Landsat time series. Remote Sensing of Environment, 288, 113483.

Saatchi SS, Harris NL, Brown S et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108, 9899–9904. 

Baccini A, Goetz SJ, Walker WS et al. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2, 182–185 

Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139. 

Liang, M., Duncanson, L., Silva, J. A., & Sedano, F. (2023). Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat. Remote Sensing of Environment, 284, 113367.