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Modeling land use change and forest carbon stock changes in temperate forests in the United States / Lucia Fitts in Carbon Balance and Management, vol 16 ([01/02/2021])  

Public [article]  inCarbon Balance and Management > vol 16 [01/02/2021] . - n° 20 (2021) Titre : Modeling land use change and forest carbon stock changes in temperate forests in the United States Type de document : Article/Communication Auteurs : Lucia Fitts, Auteur ; Matthew B. Russell, Auteur ; Grant M. Domke, Auteur ; Joseph F. Knight, Auteur Année de publication : 2021 Article en page(s) : n° 20 (2021) Langues : Anglais (eng) Descripteur : [Termes IGN] changement d'occupation du sol [Termes IGN] Colorado (Etats-Unis) [Termes IGN] forêt tempérée [Termes IGN] Géorgie (Etats-Unis) [Termes IGN] impact sur l'environnement [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modélisation spatio-temporelle [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] puits de carbone [Termes IGN] Texas (Etats-Unis) [Termes IGN] Wisconsin (Etats-Unis) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Background : Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine. Results : During the study period (2000–2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change. Conclusions : Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions. Numéro de notice : A2021-501 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article date de publication en ligne : 03/07/2021 En ligne : https://doi.org/10.1186/s13021-021-00183-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98099 [article]

Classification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4939 - 4950 Titre : Classification of hyperspectral and LiDAR data using coupled CNNs Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Zhu Li, Auteur ; Pedram Ghamisi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4939 - 4950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données hétérogènes [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] Houston (Texas) [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] Perceptron multicouche [Termes IGN] précision de la classification [Termes IGN] semis de points [Termes IGN] Trente [Termes IGN] utilisation du sol Résumé : (auteur) In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral–spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter-sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy (OA) of 96.03%. On the Trento data, it achieves an OA of 99.12%. These results sufficiently certify the effectiveness of our proposed model. Numéro de notice : A2020-391 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2969024 date de publication en ligne : 06/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2969024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95374 [article]

Placial analysis of events: a case study on criminological places / Sunghwan Cho in Cartography and Geographic Information Science, Vol 46 n° 6 (November 2019)  

Public [article]  inCartography and Geographic Information Science > Vol 46 n° 6 (November 2019) . - pp 547-566 Titre : Placial analysis of events: a case study on criminological places Type de document : Article/Communication Auteurs : Sunghwan Cho, Auteur ; May Yuan, Auteur Année de publication : 2019 Article en page(s) : pp 547-566 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] cartographie statistique [Termes IGN] criminalité [Termes IGN] Dallas (Texas) [Termes IGN] détection d'événement [Termes IGN] données spatiotemporelles [Termes IGN] géolocalisation [Termes IGN] interaction humain-espace [Termes IGN] système d'information géographique [Termes IGN] zone à risque Résumé : (auteur) The contrast of space and place has long been an active topic of scholarly discussions in many disciplines. While spatial analysis enjoys a multitude of quantitative methods, the study of place remains mostly conceptual and descriptive. This paper expands upon the rich concepts of place in the literature to propose a quantitative framework for placial analysis based on events. Central to the proposed framework are three assumptions: (1) human experiences transform space to place; (2) events build human experiences in space; and (3) places emerge organically and may change characters, spatial extent and location over time through the shifts in occurrences and types of events in space and time. The proposed framework consists of three elements: clustering events, decomposing event distributions, and identifying the similarity of event clusters. We applied the framework to identify criminological places in the City of Dallas in the United States and the changes of these places from 1 June 2014 to 30 May 2018. Numéro de notice : A2019-417 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1578265 date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.1080/15230406.2019.1578265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93544 [article]

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Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA / Christopher B. Edgar in Forest ecology and management, vol 437 (1 April 2019)  

Public [article]  inForest ecology and management > vol 437 (1 April 2019) . - pp 27-40 Titre : Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA Type de document : Article/Communication Auteurs : Christopher B. Edgar, Auteur ; James A. Westfall, Auteur ; Paul A. Klockow, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 27-40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation temporelle [Termes IGN] analyse diachronique [Termes IGN] arbre mort [Termes IGN] catastrophe naturelle [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données dendrométriques [Termes IGN] échantillonnage [Termes IGN] gestion forestière [Termes IGN] insecte nuisible [Termes IGN] inventaire forestier étranger (données) [Termes IGN] jeu de données [Termes IGN] maladie phytosanitaire [Termes IGN] Pinus (genre) [Termes IGN] politique forestière [Termes IGN] Quercus (genre) [Termes IGN] sécheresse [Termes IGN] tempête [Termes IGN] Texas (Etats-Unis) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Understanding the impacts of large-scale disturbances on forest conditions is necessary to support forest management, planning, and policy decision making. National forest inventories (NFIs) are an important information source that provide consistent data encompassing large areas, covering all ownerships, and extending through time. Here we compare how temporal aggregation approaches with NFI data affects estimates of standing dead trees as these respond to extreme disturbance events. East Texas was selected for this study owing to the occurrence of three significant disturbance events in a short span: Hurricane Rita in 2005, Hurricane Ike in 2008, and a historic drought in 2011. Wide-spread tree damage and mortality were reported after each disturbance and estimates of standing dead trees were used as the inventory variable for assessment. In the NFI of the US, the plot network is systematically divided into panels and one panel is measured each year. A measurement cycle is completed when all panels have been measured, which varies between 5 and 10 years depending on the region. Using the standard estimation approach of the US NFI, we computed population estimates using data from the full set of panels (FSP), multiple sets of panels (MSP), and single set of panels (SSP). For estimation, a single plot observation is computed from the most recent measurement of the plot that does not exceed the estimate year. Because one panel is measured per year, FSP and MSP estimates will necessarily consist of plot observations whose measurements were collected over a number of years. The SSP estimate is computed from one panel and thus all the plot observations are based on measurements collected over one year. We found that interpretations of disturbance event impacts varied depending on which sets of estimates were considered. All sets of estimates suggested a large and significant drought impact. However, differences existed among the estimates in the timing and magnitude of the impacts. The FSP estimates showed clear lag bias and smoothing of trends relative to the SSP estimates. MSP estimates were intermediate between FSP and SSP estimates. Differences in Hurricane Rita impacts were also observed between sets of estimates. Evidence of a net impact on standing dead trees following Hurricane Ike was weak among all sets of estimates. Given the potential for lag bias and smoothing, we recommend that SSP and MSP estimates be considered along with FSP estimates in assessments of large-scale disturbance impacts on forest conditions. Numéro de notice : A2019-483 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2019.01.027 date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.foreco.2019.01.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93659 [article]

Integrating urban and national forest inventory data in support of rural–urban assessments / James A. Westfall in Forestry, an international journal of forest research, vol 91 n° 5 (December 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 5 (December 2018) . - pp 641 - 649 Titre : Integrating urban and national forest inventory data in support of rural–urban assessments Type de document : Article/Communication Auteurs : James A. Westfall, Auteur ; Paul L. Patterson, Auteur ; Christopher B. Edgar, Auteur Année de publication : 2018 Article en page(s) : pp 641 - 649 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation [Termes IGN] Austin (Texas) [Termes IGN] intégration de données [Termes IGN] inventaire forestier étranger (données) [Termes IGN] inventaire forestier local [Termes IGN] Texas (Etats-Unis) [Termes IGN] variance [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Due to the interest in status and trends in forest resources, many countries conduct a national forest inventory (NFI). To better understand the characteristics of woody vegetation in areas that are typically not forested, there is an increasing emphasis on urban inventory efforts where all trees both within and outside forest areas are measured. Often, these two inventories are entirely independent endeavours from data collection through analytical reporting. To holistically explore landscape-scale phenomena across the rural–urban gradient, there is a need to combine information from both sources. In this paper, methods for combining these two data sources are examined using data from an urban inventory conducted in Austin, Texas, USA, and NFI data collected in the same and surrounding areas. Approaches to aggregating areas based on sampling intensity and plot design combinations are of considerable importance for the validity of the estimation. An additional complexity can also arise due to temporal discrepancies between the two data sources. Thus, it is imperative to accurately identify all the existing sampling intensity/plot design combinations within the population of interest. Once this difficulty is surmounted, there still exist aggregation methods that will produce erroneous results. Statistically valid variance estimation arises from maintaining independence of the two samples. This approach satisfies both the proportional allocation among strata requirement as well as the necessary partitioning of the two plot designs. Difficulty in interpretation of results can also be encountered due to differences in measurement protocols across aggregated areas. Thus, analysts should have an in-depth understanding of data sources and the differences between them to avoid unintended errors. The need for rural–urban assessments are expected to increase dramatically as urban areas expand and issues such as land conversion, wildland fire and invasive species spread become of further importance. Numéro de notice : A2018-638 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpy023 date de publication en ligne : 20/07/2018 En ligne : https://doi.org/10.1093/forestry/cpy023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93247 [article]

Spatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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A spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)  

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A posteriori bias correction of three models used for environmental reporting / Bogdan M. Strimbu in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)  

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Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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On the spectral combination of satellite gravity model, terrestrial and airborne gravity data for local gravimetric geoid computation / Tao Jian in Journal of geodesy, vol 90 n° 12 (December 2016)  

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Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas / Colin Robertson in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

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Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis / Marco Helbich in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)  

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Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth / Khila R. Dahal in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)  

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An entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)  

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Developing an object-based hyperspatial image classifier with a case study using WorldView-2 data / Harini Sridharan in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

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