High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar

Fernández-Landa, Alfredo; Fernández-Moya, Jesús; Tomé, José Luis Pensado; Algeet-Abarquero, Nur; Guillén-Climent, María Luz; Vallejo, Roberto; Sandoval, Vicente; Marchamalo, Miguel

doi:10.1080/01431161.2018.1430406

Cited by 21 publications

(14 citation statements)

References 26 publications

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“…The TV and IATV models used by Tomé Morán et al [75] in Murcia yielded rRMSE values of respectively 47.2% and 44.4% for P. halepensis forests and rRMSE values of respectively 45.8% and 43.9% for mixed stands of pines (P. nigra, P. halepensis and P. pinaster). We only found one study [26], carried out in La Rioja, which reported values in the same range as those obtained in the present study (R 2 = 0.75 and rRMSE = 26.1-32.3 for P. sylvestris).…”

Section: Discussionsupporting

confidence: 86%

“…Thus, for example, Murgaš et al [41] reported that up to 10% of the plots had positioning errors of 20 m or more within inventories of selected management units in Slovakia. As revisiting SNFI plots to capture more accurate coordinates is very costly and time-consuming, Spanish authorities are planning to capture new coordinates with errors less than 1 m in the next re-measurement of SNFI plots [26,42], thus making it easier to combine the field plot data with the information provided by remote sensing systems [34].…”

Section: Harmonization Of Snfi and Als Datamentioning

confidence: 99%

“…Moreover, ALS data, which cover the whole country, have been compiled by the PNOA-LiDAR project of the Instituto Geográfico Nacional (IGN) since 2008 with multipurpose objectives, e.g., obtaining digital elevation, surface or hydrographic models, and automatic detection of terrain modifications. Despite the availability of this very valuable information, very few studies have used both types of information together (e.g., [4,25,26]), mainly because of the following important challenges: (i) differences in time of acquisition of both types of data in most regions, and (ii) problems associated with the precise geolocation of SNFI plots.…”

Section: Introductionmentioning

confidence: 99%

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Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

et al. 2019

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The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8-38.3%, 34.2-41.9% and 31.7-38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

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Section: Discussionsupporting

confidence: 86%

Section: Harmonization Of Snfi and Als Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

et al. 2019

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“…The main difficulty when using plots from the SNFI in combination with ALS data is the lack of precision in the plot center coordinates. Global navigation satellite system (GNSS) devices used in SNFI data collection, as other studies showed [46,47], have a nominal accuracy of 5-15 m. Following the methodology and the information sources proposed by Reference [48], the selected plots were relocated manually to improve the accuracy of the plot center coordinates. In this case, plots were shifted an average of 12.5 m in the relocation procedure.…”

Section: Field Datamentioning

confidence: 99%

Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale

Durante

Martín-Alcón²,

Gil‐Tena³

et al. 2019

Remote Sensing

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Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.

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“…Slope and aspect are two of the most important topographic metrics affecting natural processes, applications and costs in forestry (Aruga et al, 2007;Gonga-Saholiariliva et al, 2011;Fernández-Landa et al, 2018;Lidberg et al, 2019). In this context, the area of the slope and aspect classes and the areal differences from the reference dataset were calculated for each data set.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

Yurtseven¹

2019

Eurasian Journal of Forest Science

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DEMs (Digital Elevation Model) generated with different remote sensing techniques and technologies. DEMs are used to determine the changes of vegetation in forests depending on topographical factors. The accuracy of DEMs has a major impact on the planning and management of forests. In this study, the accuracy of two different DEM data sources, which are frequently used in the modeling of topographic changes in large field studies in forestry, was compared with the LiDAR-based DEM dataset on a forest site. In this context, three different DEM source were used. One of them was the 10 m interval contour lines of 1:25,000 scale aerial photogrammetry based standard topographical maps which are produced by National General Directorate of Mapping. Topomap contour lines are transformed to grid based DEMs by using TIN and ANUDEM based approaches at 2.5, 5, 10 and 30 m resolutions. The other data was ASTER GDEM (1 arc-second ASTER GDEM Version 2, approximately 30 m resolution). The final and reference data is the LiDAR based, 0.25 m resolution DEM. In total, 33 DEM datasets are compared with the LiDAR-based DEM dataset. For these data sets, five difference metrics were calculated: pixel based difference models, the areal and volumetric difference of surface models, the areal difference of slope classes and the areal difference of aspect classes. According to the results of the analysis, the resolution, according to the topographic characteristics of the area and selected interpolation approaches has an effect on DEM modeling and DEM-derived metrics. In addition, the forest structure has a major impact on the accuracy of ASTER GDEM data.

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High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar

Cited by 21 publications

References 26 publications

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

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