Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Wulder, Michael A.; Han, Tong; White, Joanne C.; Sweda, Tatsuo; Tsuzuki, Hayato

doi:10.1016/j.rse.2007.02.002

Cited by 70 publications

(32 citation statements)

References 25 publications

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“…Applications of remote sensing aimed at monitoring structural attributes of forests that were listed above have been driven largely by using empirical models to calibrate remotely sensed data with in situ data in either boreal or other forest ecosystems (Table 7, [8,28,112,187,196,[202][203][204][205][206][207][208][209][210][211][212][213]). For example, regression-based prediction has been a widely accepted approach to mapping regional forest attributes using linear regression [196,203,204,213], nonlinear regression [187,[209][210][211]214,215], partial least squares regression [216] and regression tree algorithms [26,217].…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

“…For example, regression-based prediction has been a widely accepted approach to mapping regional forest attributes using linear regression [196,203,204,213], nonlinear regression [187,[209][210][211]214,215], partial least squares regression [216] and regression tree algorithms [26,217]. Recently, non-parametric regression approaches, such as Reduced Major Axis (RMA) regression, k-Nearest Neighbor (k-NN), Gradient Nearest Neighbor (GNN) and Random Forest (RF) regressions, have received considerable attention for the estimation of structural forest attributes, because these approaches can account for mapping uncertainty and involve a large number of response variables with analytical and operational flexibility [28,209,218].…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

“…The study also indicated that the mean regenerated post-fire canopy heights had a positive high correlation with the number of years since the fire. Although airborne LiDAR and SAR data have been successfully used to derive structural attributes of vegetation, an alternative approach to mapping forest attributes is to use these measurements in combination with remotely sensed optical imagery [207,[209][210][211][212]220]. This synergistic use of active and passive sensors provides opportunities to fully characterize the structural attributes and dynamics of forests with regard to disturbance regimes (e.g., burn severity) and pre-and post-fire vegetation types (Table 7).…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

2013

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Abstract:The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre-and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

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Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

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Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

2013

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“…However, the acquisition of full wall-to-wall LiDAR data coverage over large areas is rare (Nelson et al , 2005Naesset et al 2004). Possible ways increase the cost-effectiveness of LiDAR acquisition include cost-sharing consortia with multiple stakeholders (Reutebuch et al 2005), and strategic combinations of LiDAR samples (e.g., transects) with wall-to-wall image coverage, such as using aerial photography or moderate to high spatial resolution satellite imagery (e.g., Landsat or QuickBird) (Hudak et al 2002, Wulder and Seemann 2003, Wulder et al 2007b, and thereby extrapolating structural information across the larger area based on empirical relationships between the spectral properties of the canopy and the LiDAR data.…”

Section: Operational Considerationsmentioning

confidence: 99%

The role of LiDAR in sustainable forest management

Wulder¹,

Bater²,

Coops³

et al. 2008

The Forestry Chronicle

318

186

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Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.Key words: LiDAR, airborne laser altimetry, forest inventory, height, volume, biomass, update, remote sensing RÉSUMÉ La représentation des forêts à partir de données LiDAR (détection de la lumière et calcul de la distance) a attiré dernière-ment beaucoup d'attention tant scientifique qu' opérationnelle. Le nombre de variables d'inventaire forestier qui peuvent être mesurées directement par LiDAR est limité, mais lorsqu' on considère le contexte de toutes les variables mesurées et dérivées requises pour compléter un inventaire forestier, le LiDAR peut constituer un outil précieux du processus d'inventaire. Nous présentons dans cet article un portrait de la télédétection des forêts par LiDAR, ainsi que les questions portant sur l'appareillage, la collecte des données, les coûts et l' estimation des variables. Les besoins d'information en matière d'aménagement forestier durable constituent le contexte que nous retenons pour les possibilités d'application future du LiDAR et du traitement automatisé des données.

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“…Forest AGB dynamics can be described as continuous or gradual (i.e., growth) and discontinuous or abrupt (i.e., disturbance) variations (Wulder et al, 2007) that, together, result in variations in the productivity and carbon fluxes of forests (Misson et al, 2005;Main-Knorn et al, 2013). Disturbances include fires and other natural disasters, and tree felling or planting.…”

Section: Introductionmentioning

confidence: 99%

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Tian

Chen

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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A B S T R A C TIn this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China.Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R 2 = 0.70 and RMSE = 24.52 tones ha −1 ). We then obtained one seasonal cycle ( ) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (BiomeBioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R 2 = 0.75 and RMSE = 1. 27 gC m −2 d −1 for GPP, and R 2 = 0.61 and RMSE = 1.17 gC m −2 d −1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R 2 = 0.73 and RMSE = 46.65 g m −2 year −1 ). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.

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Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Cited by 70 publications

References 25 publications

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

The role of LiDAR in sustainable forest management

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

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