Show source-provided references
1. The high subarctic forest-tundra of northwestern Canada: Position, width, and vegetation gradients in relation to climate
10.14430/arctic1367 ↗2. Ecosystem Classification Group (2007). Ecological Regions of the Northwest Territories–Taiga Plains.
3. Variations in soil microbial community structure and extracellular enzymatic activities along a forest–wetland ecotone in high-latitude permafrost regions
10.1002/ece3.10205 ↗4. The transition zones (ecotone) between boreal forests and peatlands: Ecological controls on ecosystem productivity along a transition zone between upland black spruce forest and a poor forested fen in central Saskatchewan
10.1016/j.ecolmodel.2014.07.020 ↗5. Rooting of peatland black spruce and tamarack in relation to depth of water table
10.1139/b87-111 ↗7. Carbon distribution and aboveground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada
10.1029/97JD02317 ↗8. Timoney, K.P. (2013). The Peace-Athabasca Delta: Portrait of a Dynamic Ecosystem, University of Alberta.
10.1515/9780888648020 ↗9. Enayetullah, H., Chasmer, L., Hopkinson, C., Thompson, D., and Cobbaert, D. (2022). Identifying Conifer Tree vs. Deciduous Shrub and Tree Regeneration Trajectories in a Space-for-Time Boreal Peatland Fire Chronosequence Using Multispectral Lidar. Atmosphere, 13.
10.3390/atmos13010112 ↗10. Jean, S.A., Pinno, B.D., and Nielsen, S.E. (2020). Early regeneration dynamics of pure black spruce and aspen forests after wildfire in boreal Alberta, Canada. Forests, 11.
10.3390/f11030333 ↗11. Mitsch, W.J., and Gosselink, J.G. (2015). Wetlands, John Wiley & Sons.
13. Decline of an ecotone forest: 50 years of demography in the southern boreal forest
10.1002/ecs2.2698 ↗15. Tall shrub and tree expansion in Siberian tundra ecotones since the 1960s
10.1111/gcb.12406 ↗16. Expansion of subalpine woody vegetation over 40 years on Vancouver Island, British Columbia, Canada
10.1139/cjfr-2015-0186 ↗17. Chasmer, L., Cobbaert, D., Mahoney, C., Millard, K., Peters, D., Devito, K., Brisco, B., Hopkinson, C., Merchant, M., and Montgomery, J. (2020). Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management. Remote Sens., 12.
10.3390/rs12081320 ↗24. Vegetation productivity patterns at high northern latitudes: A multi-sensor satellite data assessment
10.1111/gcb.12647 ↗25. Lebre, D. (2019). Differences in Vegetation Composition and Structure at Greening and Non-Greening Sites in the Northwest Territories. [Ph.D. Thesis, Queen’s University].
27. Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening
10.1016/j.envres.2022.115155 ↗28. The use of Landsat data for investigating the long-term trends in wetland change at Long Point, Ontario
10.5589/m05-012 ↗29. Recent (1986–2006) vegetation-specific NDVI trends in northern Canada from satellite data
30. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest
10.3390/s7112636 ↗31. Garroutte, E.L., Hansen, A.J., and Lawrence, R.L. (2016). Using NDVI and EVI to Map Spatiotemporal Variation in the Biomass and Quality of Forage for Migratory Elk in the Greater Yellowstone Ecosystem. Remote Sens., 8.
10.3390/rs8050404 ↗36. A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert, USA
10.1080/01431161.2011.592865 ↗37. Satellite observations document trends consistent with a boreal forest biome shift
10.1111/gcb.16121 ↗38. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011
10.3390/rs5104799 ↗39. Estimating soil moisture conditions of the greater Changbai Mountains by land surface temperature and NDVI
10.1109/TGRS.2010.2040830 ↗40. Long-term relationships of MODIS NDVI with rainfall, land surface temperature, surface soil moisture and groundwater storage over monsoon core region of India
10.1080/15324982.2022.2106323 ↗41. Snow cover change and its relationship with land surface temperature and vegetation in northeastern North America from 2000 to 2017
10.1080/01431161.2020.1779379 ↗42. Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India
10.1007/s00477-021-02066-1 ↗47. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., and Mitchard, E.T.A. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sens., 8.
10.3390/rs8010070 ↗48. Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment
10.5589/m05-007 ↗49. Lidar plots—A new large-area data collection option: Context, concepts, and case study
10.5589/m12-049 ↗50. Montgomery, J., Brisco, B., Chasmer, L., Devito, K., Cobbaert, D., and Hopkinson, C. (2019). SAR and LiDAR temporal data fusion approaches to boreal wetland ecosystem monitoring. Remote Sens., 11.
10.3390/rs11020161 ↗52. Chasmer, L., Mahoney, C., Millard, K., Nelson, K., Peters, D., Merchant, M., Hopkinson, C., Brisco, B., Niemann, O., and Montgomery, J. (2020). Remote sensing of boreal wetlands 2: Methods for evaluating boreal wetland ecosystem state and drivers of change. Remote Sens., 12.
10.3390/rs12081321 ↗53. Evidence of vegetation greening at alpine treeline ecotones: Three decades of Landsat spectral trends informed by lidar-derived vertical structure
10.1088/1748-9326/aad5d2 ↗55. Jones, E.A., Chasmer, L.E., Devito, K.J., and Hopkinson, C.D. (2024). Shortening fire return interval predisposes west-central Canadian boreal peatlands to more rapid vegetation growth and transition to forest cover. Glob. Change Biol., 30.
10.1111/gcb.17185 ↗56. Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data
10.1016/j.rse.2016.04.010 ↗57. Ji, P., Su, R., Wu, G., Xue, L., Zhang, Z., Fang, H., Gao, R., Zhang, W., and Zhang, D. (2025). Projecting Future Wetland Dynamics Under Climate Change and Land Use Pressure: A Machine Learning Approach Using Remote Sensing and Markov Chain Modeling. Remote Sens., 17.
10.3390/rs17061089 ↗58. Gxokwe, S., Dube, T., and Mazvimavi, D. (2020). Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions. Remote Sens., 12.
10.3390/rs12244190 ↗59. Pan, F., Xie, J., Lin, J., Zhao, T., Ji, Y., Hu, Q., Pan, X., Wang, C., and Xi, X. (2018). Evaluation of Climate Change Impacts on Wetland Vegetation in the Dunhuang Yangguan National Nature Reserve in Northwest China Using Landsat Derived NDVI. Remote Sens., 10.
10.20944/preprints201803.0233.v1 ↗60. Ma, Z., Chen, W., Xiao, A., and Zhang, R. (2023). The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes. Remote Sens., 15.
10.3390/rs15184534 ↗62. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change
10.4155/cmt.11.18 ↗64. Ramsar Convention (2024, February 01). An Introduction to the Ramsar Convention on Wetlands, 7th ed. Available online: https://www.ramsar.org/sites/default/files/documents/library/handbook1_5ed_introductiontoconvention_final_e.pdf.
68. Ducks Unlimited Canada (2020). Wood Buffalo National Park Enhanced Wetland Classification User’s Guide, Ducks Unlimited Canada.
71. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity
10.1016/j.rse.2015.12.024 ↗72. Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., and Zhu, Z. (2018). Making Landsat time series consistent: Evaluating and improving Landsat analysis ready data. Remote Sens., 11.
10.3390/rs11010051 ↗75. USGS (2024, April 01). Landsat Collection 2, Available online: https://pubs.usgs.gov/fs/2021/3002/fs20213002.pdf.
76. Assessment of airborne scanning laser altimetry (lidar) in a deltaic wetland environment
10.5589/m03-040 ↗79. Examining the effects of sampling point densities on laser canopy height and density metrics
10.5558/tfc84876-6 ↗81. Hemati, M., Hasanlou, M., Mahdianpari, M., and Mohammadimanesh, F. (2021). A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sens., 13.
10.3390/rs13152869 ↗82. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform
10.1016/j.isprsjprs.2018.07.017 ↗83. Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
10.1080/15481603.2019.1690780 ↗84. Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud
85. Aslami, F. (2024). Lidar Derived Models and NDVI Trends Indicate Vegetation Threshold Response to Hydroclimatic Drivers Across the Peace Athabasca Delta. [Master’s Thesis, University of Lethbridge].
87. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
10.1080/01431160600589179 ↗89. A rank-invariant method of linear and polynomial regression analysis
91. Kang, Y., Guo, E., Wang, Y., Bao, Y., Bao, Y., and Mandula, N. (2021). Monitoring vegetation change and its potential drivers in Inner Mongolia from 2000 to 2019. Remote Sens., 13.
10.3390/rs13173357 ↗93. Mancino, G., Console, R., Greco, M., Iacovino, C., Trivigno, M.L., and Falciano, A. (2022). Assessing vegetation decline due to pollution from solid waste management by a multitemporal remote sensing approach. Remote Sens., 14.
10.3390/rs14020428 ↗94. Eastman, J.R. (2015). TerrSet, Geospatial Monitoring and Modeling Software, Clark Labs, Clark University.
96. Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach
10.1016/j.scitotenv.2019.06.056 ↗97. Landsat-based multi-decadal spatio-temporal assessment of the vegetation greening and browning trend in the Eastern Indian Himalayan Region
98. Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series
10.5721/EuJRS20134603 ↗99. Long-Term and Fine-Scale Surface Urban Heat Island Dynamics Revealed by Landsat Data Since the 1980s: A Comparison of Four Megacities in China
10.1029/2021JD035598 ↗100. Andronis, V., Karathanassi, V., Tsalapati, V., Kolokoussis, P., Miltiadou, M., and Danezis, C. (2022). Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus. Remote Sens., 14.
10.3390/rs14041010 ↗