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How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4551 - 4569 Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] auscultation topographique [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Minnesota (Etats-Unis) [Termes descripteurs IGN] modèle d'erreur [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] surface forestière [Termes descripteurs IGN] télémètre laser aéroporté Résumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535 [article]

Co-registration and inter-sensor comparison of MODIS and LANDSAT 7 ETM+ data aimed at NDVI calculation / P. Boccardo in Revue Française de Photogrammétrie et de Télédétection, n° 182 (Juin 2006)

Public [article]  inRevue Française de Photogrammétrie et de Télédétection > n° 182 (Juin 2006) . - pp 74 - 79 Titre : Co-registration and inter-sensor comparison of MODIS and LANDSAT 7 ETM+ data aimed at NDVI calculation Type de document : Article/Communication Auteurs : P. Boccardo, Auteur ; Enrico Borgogno Mondino, Auteur ; et al., Auteur Congrès : Congrès: ISPRS 2006 - Commission 1 Symposium Des capteurs à l'imagerie = From sensors to imagery (3 - 6 juillet 2006; Champs-sur-Marne in Marne-la-Vallée, France), Commanditaire Année de publication : 2006 Article en page(s) : pp 74 - 79 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] homologie [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] limite de résolution géométrique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] superposition d'images Résumé : (Auteur) To evaluate accuracy of low resolution vegetation mapping for hydrological purposes, a comparative study of NDVI images derived from MODIS and Landsat 7 ETM+ data has been done. Main goal is to understand how effective MODIS images can be for vegetation characterization on large areas, as compared to the Landsat 7 ETM+ ones. In this paper a methodology is proposed with the aim of measuring the difference between NDVI values derived from the two different data, considering synthetic parameters and investigating their dependency on the geometric resolution of the images. Great attention was paid to the problem of the geometric co-registration of the two types of data. This is a very sensitive parameter for the subsequent analysis. A mixed approach was adopted: images were firstly orthoprojected to eliminate sensor geometry and relief displacement effects; subsequently, a refining image-to-image co-registration procedure was carried out through a homographic transformation implemented in a self-developed routine. Two pairs of contemporary images (MODIS and Landsat 7) were used as benchmarks for our tests. Simplified procedures aimed at calibrating images and at removing atmospheric noise were performed. The resulting corrected images were used to calculate NDVI images. These ones (two pairs) were then compared through a statistical approach in order to investigate how a different geometric resolution can influence the NDVI values. The proposed approach is not a traditional image based (matrix comparison) but a new one. NDVI value correspondences were considered between the MODIS pixel and the group of Landsat pixels belonging to the polygon which represents the considered MODIS pixel in the Landsat image space. Statistics extracted on-the-fly from these Landsat pixels were used to investigate in depth the relationship between them and NDVI value of the corresponding MODIS pixel. NDVI differences were calculated between the single NDVI MODIS values and a synthetic parameter (mean value) of the homologous Landsat pixel group. A direct comparison between the NDVI values obtained from MODIS and Landsat 7 images has shown a systematic error that can be read as bias (MODIS NDVI over estimation). This led the authors to determine a suitable model in order to eliminate the bias, whose presence would have conditioned later comparisons. Original MODIS image was then corrected through the defined model. This has been designed to be suitable for any MODIS image acquired over the same area (parameterization was used). New NDVI differences were calculated using the corrected MODIS images and the previous Landsat 7 ones. In order to investigate the nature of the residual differences and to try to recognize the critical MODIS pixels, some considerations were made concerning the statistics of each corresponding group of Landsat pixels. A classification of the MODIS pixel was generated according to the behaviour of their differences with respect to the adopted statistics. Copyright SFPT Numéro de notice : A2006-624 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28347 [article]

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