Centre de documentation
scientifique

Intra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 151 - 161 Titre : Intra-annual phenology for detecting understory plant invasion in urban forests Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Yin-Hsuen Chen, Auteur ; Lindsey Smart, Auteur ; Josh Gray, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2018 Article en page(s) : pp 151 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Caroline du Nord (Etats-Unis) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de la végétation [Termes IGN] détection d'anomalie [Termes IGN] espèce exotique envahissante [Termes IGN] flore urbaine [Termes IGN] forêt tempérée [Termes IGN] image Landsat-TM [Termes IGN] indice de végétation [Termes IGN] Ligustrum sinense [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] protection de la biodiversité [Termes IGN] surveillance forestière Résumé : (Auteur) Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Numéro de notice : A2018-293 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.023 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90411 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Effects of LiDAR point density and landscape context on estimates of urban forest biomass / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 101 (March 2015) . - pp 310 - 322 Titre : Effects of LiDAR point density and landscape context on estimates of urban forest biomass Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Gang Chen, Auteur ; James B. McCarter, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2015 Article en page(s) : pp 310 - 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse [Termes IGN] Caroline du Nord (Etats-Unis) [Termes IGN] composition d'un peuplement forestier [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuillu [Termes IGN] forêt urbaine [Termes IGN] régression multiple Résumé : (auteur) Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density. Numéro de notice : A2015-471 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.12.021 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.12.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77173 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Landscape dynamics of the spread of sudden oak death / M. Kelly in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 10 (October 2002)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 10 (October 2002) . - pp 1001 - 1009 Titre : Landscape dynamics of the spread of sudden oak death Type de document : Article/Communication Auteurs : M. Kelly, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2002 Article en page(s) : pp 1001 - 1009 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de groupement [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] distribution spatiale [Termes IGN] dynamique spatiale [Termes IGN] Fagus (genre) [Termes IGN] forêt [Termes IGN] image ADAR [Termes IGN] image multibande [Termes IGN] indice d'humidité [Termes IGN] maladie phytosanitaire [Termes IGN] modélisation spatiale [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] simulation dynamique Résumé : (Auteur) Sudden Oak Death is caused by a newly discovered virulent pathogen (Phytophthora ramorum) that is killing thousands of native oak trees in California. We present a landscape-scale study on the spatio-temporal dynamics of oak mortality. Second-order spatial point-pattern analysis techniques (Ripley's K) were applied to the distribution of dead tree crowns (derived from high-resolution imagery) in Marin County, California to determine the existence and scale of mortality clustering in two years (2000 and 2001). Both years showed clustering patterns between 100 and 300 m. A classification tree model was developed to predict spatial patterns of risk for oak mortality based on several landscapescale variables. Proximity to forest edge was the most important explanatory factor, followed by topographic moisture index, proximity to trails, abundance of Umbellularia californica, and potential summer solar radiation. This research demonstrates the utility of integrating remotely sensed imagery analysis with geographic information systems and spatial modeling for understanding the dynamics of exotic species invasions. Numéro de notice : A2002-233 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/october/2002_oct_1001- [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22147 [article]