Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots

Matasci, Giona; Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Hobart, Geordie W.; Bolton, Douglas K.; Tompalski, Piotr; Bater, Christopher W.

doi:10.1016/j.rse.2018.07.024

Cited by 116 publications

(89 citation statements)

References 104 publications

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“…To our knowledge, this is the first time spatial change metrics extracted from Landsat data have been combined with the systematic network of forest inventory, across large areas of sclerophyll forests in Victoria, Australia. Our results from the variable importance analysis were consistent with those from previous studies [6,9,16,19,20,63]. Spectral indices such as NBR and TCA were the most important variables overall, and change metrics such as disturbance and recovery magnitude, and TSD, were particularly important for modelling dead biomass and structure variables.…”

Section: Discussionsupporting

confidence: 89%

“…Maintaining consistent parameters (i.e., k value) and methods allows the imputation results to be evaluated more effectively [32]. We note also, that the use of a single nearest neighbour has been increasing in forest applications with kNN models, particularly in biomass estimation [15,16,19,29,36,63]. Chirici, Mura, McInerney, Py, Tomppo, Waser, Travaglini and McRoberts [36] found in their review work that k = 1 is the most common selection to use with MSN and GNN techniques.…”

Section: Discussionmentioning

confidence: 99%

“…This is reasonable since these techniques are initially created to use with the single nearest neighbour, as mentioned in the introduction. Recent studies using the RF technique also often selected k = 1 for their imputation models [16,19,63]. As forest inventory programs are increasingly developed systematically, measurements from inventory plots are representatives of forest populations [64].…”

Section: Discussionmentioning

confidence: 99%

“…Similar to Zald, Wulder, White, Hilker, Hermosilla, Hobart and Coops [19], our results indicate that change attribution variables (disturbance level and causal agent) are less important since fire is the dominant disturbance within the study area. However, these metrics may greatly benefit kNN imputation models by distinguishing pixels with similar spectral information [19,63].…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

et al. 2018

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The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987-2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

et al. 2018

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“…8). La disponibilidad de series temporales de datos procedentes de sensores remotos hace que sea posible evaluar la dinámica de las masas forestales y sus productos (Matasci et al 2018;Zhao et al 2018…”

Section: Integración De Imágenes De Satélite Y Datos Lidar Como Herraunclassified

Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

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Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

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et al. 2020

Ecological Applications

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Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsatderived normalized difference vegetation index (NDVI). The best-performing model accounted for inter-and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

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Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots

Cited by 116 publications

References 104 publications

A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

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