journal article·2025-04-23Open access

Using Bi-Temporal Lidar to Evaluate Canopy Structure and Ecotone Influence on Landsat Vegetation Index Trends Within a Boreal Wetland Complex

Farnoosh Aslami, Chris Hopkinson, Laura Chasmer, Craig Mahoney, Daniel L. Peters

Applied Sciences· Volume 15· Issue 9

Abstract

Wetland ecosystems are sensitive to climate variation, yet tracking vegetation type and structure changes through time remains a challenge. This study examines how Landsat-derived vegetation indices (NDVI and EVI) correspond with lidar-derived canopy height model (CHM) changes from 2000 to 2018 across the wetland landscape of the Peace–Athabasca Delta (PAD), Canada. By comparing CHM change and NDVI and EVI trends across woody and herbaceous land covers, this study fills a gap in understanding long-term vegetation responses in northern wetlands. Findings show that ~35% of the study area experienced canopy growth, while 2% saw a reduction in height. CHM change revealed 11% ecotonal expansion, where shrub and treed swamps encroached on meadow and marsh areas. NDVI and EVI correlated significantly (p < 0.001) with CHM, particularly in shrub swamps (r2 = 0.40, 0.35) and upland forests (NDVI r2 = 0.37). However, EVI trends aligned more strongly with canopy expansion, while NDVI captured mature tree height growth and wetland drying, indicated by rising land surface temperatures (LST). These results highlight the contrasting responses of NDVI and EVI—NDVI being more sensitive to moisture-related changes such as wetland drying, and EVI aligning more closely with canopy structural changes—emphasizing the value of combining lidar and satellite indices to monitor wetland ecosystems in a warming climate.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Land Use and Ecosystem Services

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Geography and Environment, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

Government of Alberta, Environment and Parks, 9888 Jasper Avenue, Edmonton, AB T5J 5C6, Canada

Environment and Climate Change Canada, University of Victoria, Queenswood Campus, Victoria, BC V8N 1V8, Canada

Funding

NSERC-Discovery Grant (Natural Sciences and Engineering Research Council of Canada) · Western Economic Diversification Canada · ground survey equipment from Canada Foundation for Innovation · Environment Canada · Canada Centre for Remote Sensing · Parks Canada · Alberta Innovates graduate student scholarship

References (100+)

Show source-provided references
1. The high subarctic forest-tundra of northwestern Canada: Position, width, and vegetation gradients in relation to climate10.14430/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 regions10.1002/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 Saskatchewan10.1016/j.ecolmodel.2014.07.020 ↗
5. Rooting of peatland black spruce and tamarack in relation to depth of water table10.1139/b87-111 ↗
6. Modeling topographic effects on net ecosystem productivity of boreal black spruce forests10.1093/treephys/24.1.1 ↗
7. Carbon distribution and aboveground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada10.1029/97JD02317 ↗
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.
12. Climate change and permafrost thaw-induced boreal forest loss in northwestern Canada10.1088/1748-9326/aad74e ↗
13. Decline of an ecotone forest: 50 years of demography in the southern boreal forest10.1002/ecs2.2698 ↗
14. Recent expansion of erect shrubs in the Low Arctic: Evidence from Eastern Nunavik10.1088/1748-9326/7/3/035501 ↗
15. Tall shrub and tree expansion in Siberian tundra ecotones since the 1960s10.1111/gcb.12406 ↗
16. Expansion of subalpine woody vegetation over 40 years on Vancouver Island, British Columbia, Canada10.1139/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 ↗
18. Towards operational monitoring of a northern wetland using geomatics-based techniques10.1016/j.rse.2005.03.012 ↗
19. Red and photographic infrared linear combinations for monitoring vegetation10.1016/0034-4257(79)90013-0 ↗
20. A comparison of vegetation indices over a global set of TM images for EOS-MODIS10.1016/S0034-4257(96)00112-5 ↗
21. Development of vegetation and soil indices for MODIS-EOS10.1016/0034-4257(94)90018-3 ↗
22. Biophysical controls of increased tundra productivity in the western Canadian Arctic10.1016/j.rse.2021.112358 ↗
23. Biophysical determinants of shifting tundra vegetation productivity in the Beaufort Delta region of Canada10.1007/s10021-021-00725-6 ↗
24. Vegetation productivity patterns at high northern latitudes: A multi-sensor satellite data assessment10.1111/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].
26. Using NDVI to Assess Vegetative Land Cover Change in Central Puget Sound10.1007/s10661-006-1679-z ↗
27. Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening10.1016/j.envres.2022.115155 ↗
28. The use of Landsat data for investigating the long-term trends in wetland change at Long Point, Ontario10.5589/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 Forest10.3390/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 ↗
32. NDVI and a simple model of deciduous forest seasonal dynamics10.1016/S0304-3800(01)00354-4 ↗
33. Canopy reflectance, photosynthesis and transpiration10.1080/01431168508948283 ↗
34. Overview of the radiometric and biophysical performance of the MODIS vegetation indices10.1016/S0034-4257(02)00096-2 ↗
35. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise10.1109/TGRS.1995.8746027 ↗
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, USA10.1080/01431161.2011.592865 ↗
37. Satellite observations document trends consistent with a boreal forest biome shift10.1111/gcb.16121 ↗
38. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–201110.3390/rs5104799 ↗
39. Estimating soil moisture conditions of the greater Changbai Mountains by land surface temperature and NDVI10.1109/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 India10.1080/15324982.2022.2106323 ↗
41. Snow cover change and its relationship with land surface temperature and vegetation in northeastern North America from 2000 to 201710.1080/01431161.2020.1779379 ↗
42. Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India10.1007/s00477-021-02066-1 ↗
43. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI10.1016/j.rse.2007.09.008 ↗
44. Improving spatial soil moisture representation through integration of AMSR-E and MODIS products10.1109/TGRS.2011.2161318 ↗
45. Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data10.1016/S1002-0160(14)60031-X ↗
46. Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications10.1016/j.jhydrol.2012.10.044 ↗
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 environment10.5589/m05-007 ↗
49. Lidar plots—A new large-area data collection option: Context, concepts, and case study10.5589/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 ↗
51. Camo-maps: An efficient method to assess and project riparian vegetation colonization after a major river flood10.1016/j.ecoleng.2019.105610 ↗
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 structure10.1088/1748-9326/aad5d2 ↗
54. Mapping snowpack depth beneath forest canopies using airborne lidar10.14358/PERS.70.3.323 ↗
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 data10.1016/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 ↗
61. Human and environmental controls over aboveground carbon storage in Madagascar10.1186/1750-0680-7-2 ↗
62. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change10.4155/cmt.11.18 ↗
63. Relationship of cotton nitrogen and yield with Normalized Difference Vegetation Index and plant height10.1007/s10705-014-9640-y ↗
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.
65. Flood hydrology of the Peace—Athabasca Delta, northern Canada10.1002/hyp.6420 ↗
66. Persistence of water within perched basins of the Peace-Athabasca Delta, Northern Canada10.1007/s11273-005-1114-1 ↗
67. Regional variability and changing water distributions drive large-scale water resource availability in Alberta, Canada10.1080/07011784.2023.2186270 ↗
68. Ducks Unlimited Canada (2020). Wood Buffalo National Park Enhanced Wetland Classification User’s Guide, Ducks Unlimited Canada.
69. Continuous calibration improvement in solar reflective bands: Landsat 5 through Landsat 810.1016/j.rse.2016.07.032 ↗
70. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia10.1016/j.rse.2010.10.001 ↗
71. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity10.1016/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 ↗
73. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis10.1016/j.rse.2021.112648 ↗
74. Landsat 9: Empowering open science and applications through continuity10.1016/j.rse.2020.111968 ↗
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 environment10.5589/m03-040 ↗
77. Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment10.1080/07038992.2016.1196584 ↗
78. The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets10.1016/j.rse.2007.07.020 ↗
79. Examining the effects of sampling point densities on laser canopy height and density metrics10.5558/tfc84876-6 ↗
80. Implementation of species distribution models in Google Earth Engine10.1111/ddi.13491 ↗
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 platform10.1016/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 cloud10.1080/15481603.2019.1690780 ↗
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].
86. The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data10.1016/j.rse.2016.01.001 ↗
87. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery10.1080/01431160600589179 ↗
88. Google Earth Engine: Planetary-scale geospatial analysis for everyone10.1016/j.rse.2017.06.031 ↗
89. A rank-invariant method of linear and polynomial regression analysis
90. Estimates of the regression coefficient based on Kendall’s tau10.1080/01621459.1968.10480934 ↗
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 ↗
92. Analysing temporal trends in the ratios of PM2. 5/PM10 in the UK10.4209/aaqr.2016.02.0081 ↗
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.
95. Monotonic trend and step changes in Japanese precipitation10.1016/S0022-1694(03)00178-1 ↗
96. Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach10.1016/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 series10.5721/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 China10.1029/2021JD035598 ↗
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 ↗