Remote Sensing Technologies for Enhancing Forest Inventories: A Review

White, Joanne C.; Coops, Nicholas C.; Wulder, Michael A.; Vastaranta, Mikko; Hilker, Thomas; Tompalski, Piotr

doi:10.1080/07038992.2016.1207484

Cited by 534 publications

(350 citation statements)

References 241 publications

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“…Although the passive Landsat optical data are less sensitive to complex forest conditions [10], their continuous data collection for large scenes since 1972, moderate spatial resolution (30-m × 30-m) and synoptic coverage characteristics can improve cost-efficiency in forest inventories across large spatial and temporal domains [11,12]. Furthermore, improved accuracy of AGB prediction can be attained when Landsat data are combined with LiDAR-derived fine resolution metrics, because Landsat spectral data can more accurately predict species composition [13,14]. LiDAR data acquired via discrete or full waveform instruments, with small or large footprints and scanning or profiling characteristics, have been extensively used in past studies for AGB mapping [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, improved accuracy of AGB prediction can be attained when Landsat data are combined with LiDAR-derived fine resolution metrics, because Landsat spectral data can more accurately predict species composition [13,14]. LiDAR data acquired via discrete or full waveform instruments, with small or large footprints and scanning or profiling characteristics, have been extensively used in past studies for AGB mapping [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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“…Forest inventories provide a precise statistical estimation of forest composition for large areas, but spatially explicit information beyond inventory domains is missing [5]. Thus, maps of broadleaved and coniferous trees are an important source of information for assessing woodland resources and an additional valuable output of National Forest Inventories (NFI) [6].…”

Section: Introductionmentioning

confidence: 99%

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

2017

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Abstract:Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km 2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.

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“…Stand areas and crown cover are frequently estimated optical passive sensors. Species can also be identified with high spatial resolution images [82,[88][89][90][91].…”

Section: Forest Inventories and Stand Classificationmentioning

confidence: 99%

Multi-Species Stand Classification: Definition and Perspectives

Gonçalves¹

2017

Forest Ecology and Conservation

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The increasing demands for products and services from forests enhanced new approaches to stand composition, structure, and management, which encompass multiple use systems, frequently mixed either even aged or uneven aged. Stand classification is frequently based on one density measure (number of trees, basal area, volume or crown cover). As no standard criteria exist, the direct comparison between the different stand classifications is difficult. This created a need for a stand classification that incorporates not only the forest species and composition but also their horizontal and vertical arrangements. The four criteria stand classification incorporates the number of species and their proportion, their horizontal and vertical distribution. The application of this methodology enables an integrated approach, bridging the gap between composition and stand structure. Its use in the National Forest Inventories and in research studies is simple, as shown in the two cases of study presented. It also allows the evaluation of stands in a certain moment in time and their dynamics.

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Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Cited by 534 publications

References 241 publications

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

Multi-Species Stand Classification: Definition and Perspectives

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