Mapping and assessing land cover/land use and aboveground carbon stocks rapid changes in small oceanic islands' terrestrial ecosystems: A case study of Madeira Island, Portugal (2009–2011)

[1]  Arnaud Mialon,et al.  Satellite-observed pantropical carbon dynamics , 2019, Nature Plants.

[2]  N. Guiomar,et al.  Identifying key factors, actors and relevant scales in landscape and conservation planning, management and decision making: Promoting effective citizen involvement , 2019, Journal for Nature Conservation.

[3]  W. Anurogo,et al.  An Integrated Comparative Approach to Estimating Forest Aboveground Carbon Stock Using Advanced Remote Sensing Technologies , 2018, 2018 International Conference on Applied Engineering (ICAE).

[4]  Jonathan Li,et al.  Quantifying the Carbon Storage in Urban Trees Using Multispectral ALS Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Ana M. C. Santos,et al.  Global Island Monitoring Scheme (GIMS): a proposal for the long-term coordinated survey and monitoring of native island forest biota , 2018, Biodiversity and Conservation.

[6]  A. Gil,et al.  Land Cover Trade‐offs in Small Oceanic Islands: A Temporal Analysis of Pico Island, Azores , 2018 .

[7]  N. Guiomar,et al.  Using modeling tools for implementing feasible land use and nature conservation governance systems in small islands - The Pico Island (Azores) case-study. , 2017, Journal of environmental management.

[8]  Sinasi Kaya,et al.  Analysis of land cover/use changes using Landsat 5 TM data and indices , 2017, Environmental Monitoring and Assessment.

[9]  Majdaldin R. A.,et al.  Remote sensing-based vegetation indices for monitoring vegetation change in the semi-arid region of Sudan , 2016, Remote Sensing.

[10]  O. Mutanga,et al.  The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa , 2016 .

[11]  F. Guan,et al.  Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China , 2016 .

[12]  A. Gil.  Sustainable Development and Environmental Conservation in the Outermost European Regions , 2016, Island Studies Journal.

[13]  G. Asner,et al.  Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago , 2016, Carbon Balance and Management.

[14]  Albano Figueiredo,et al.  Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) , 2016 .

[15]  O. Mutanga,et al.  Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments , 2015 .

[16]  Ulrich Michel,et al.  Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data , 2015 .

[17]  A. Gil,et al.  Spatial planning and resource use in the Azores , 2015, Mitigation and Adaptation Strategies for Global Change.

[18]  N. Guiomar,et al.  Strategies for conservation planning and management of terrestrial ecosystems in small islands (exemplified for the Macaronesian islands) , 2015 .

[19]  Alexander Brenning,et al.  Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting , 2015 .

[20]  O. Mutanga,et al.  Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa , 2015 .

[21]  Barnali M. Dixon,et al.  Application of Support Vector Machines for Landuse Classification Using High-Resolution RapidEye Images: A Sensitivity Analysis , 2015 .

[22]  Florian Hartig,et al.  Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass , 2014 .

[23]  Birgit Kleinschmit,et al.  Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data , 2014, Remote. Sens..

[24]  Onisimo Mutanga,et al.  Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms , 2014, Sensors.

[25]  Alexey L. Pomerantsev,et al.  Principal Component Analysis (PCA) , 2014, Encyclopedia of Autism Spectrum Disorders.

[26]  Onisimo Mutanga,et al.  Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .

[27]  L. Parras-Alcántara,et al.  Land use and management effects on carbon and nitrogen in Mediterranean Cambisols , 2013 .

[28]  Patrick Hostert,et al.  Urban vegetation classification: Benefits of multitemporal RapidEye satellite data , 2013 .

[29]  R. Ruiz-Peinado,et al.  Contenido de Carbono en la biomasa de las principales especies de matorral y arbustedos de España , 2013 .

[30]  J. Rockström,et al.  Policy: Sustainable development goals for people and planet , 2013, Nature.

[31]  Pedro Pina,et al.  The 20 February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment , 2013 .

[32]  Barbara Koch,et al.  Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands , 2012 .

[33]  J. Hicke,et al.  Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery , 2012 .

[34]  B. Kleinschmit,et al.  Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data , 2012 .

[35]  F. Achard,et al.  Comparison of methods for measuring and assessing carbon stocks and carbon stock changes in terrestrial carbon pools. How do the accuracy and precision of current methods compare? A systematic review protocol , 2012, Environmental Evidence.

[36]  F. Siegert,et al.  Assessment of grassland use intensity by remote sensing to support conservation schemes , 2012 .

[37]  T. Figueiredo,et al.  Carbon storage in the Mediterranean upland shrub communities of Montesinho Natural Park, northeast of Portugal , 2012, Agroforestry Systems.

[38]  R. Trigo,et al.  The 20 February 2010 Madeira flash-floods: synoptic analysis and extreme rainfall assessment , 2012 .

[39]  Joshua B. Fisher,et al.  Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000–2008 with Landsat ETM+ and ALOS-PALSAR , 2012 .

[40]  A. Gil,et al.  Linking GMES Space Component to the development of land policies in Outermost Regions—the Azores (Portugal) case-study , 2012 .

[41]  G. Asner,et al.  Evaluating uncertainty in mapping forest carbon with airborne LiDAR , 2011 .

[42]  G. Asner,et al.  A universal airborne LiDAR approach for tropical forest carbon mapping , 2011, Oecologia.

[43]  David E. Knapp,et al.  High-resolution carbon mapping on the million-hectare Island of Hawaii , 2011 .

[44]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[45]  Pedro Pina,et al.  Automatic detection of landslide features with remote sensing techniques: Application to Madeira Island , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[46]  C. Gleason,et al.  A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications , 2011 .

[47]  R. Whittaker,et al.  A reconstruction of Palaeo‐Macaronesia, with particular reference to the long‐term biogeography of the Atlantic island laurel forests , 2011 .

[48]  M. Sequeira,et al.  Response to “Comment on fog precipitation and rainfall interception in the natural forests of Madeira Island (Portugal)” , 2010 .

[49]  Luís Silva,et al.  A global comparison of plant invasions on oceanic islands , 2010 .

[50]  Xiaorong Wei,et al.  Soil organic carbon and total nitrogen as affected by vegetation types in Northern Loess Plateau of China , 2010 .

[51]  Gregory P. Asner,et al.  Tropical forest carbon assessment: integrating satellite and airborne mapping approaches , 2009 .

[52]  Marco Schwerdt,et al.  Assessment of Atmospheric Propagation Effects in SAR Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[53]  M. Sequeira,et al.  Fog precipitation and rainfall interception in the natural forests of Madeira Island (Portugal) , 2009 .

[54]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[55]  J. Canadell,et al.  Managing Forests for Climate Change Mitigation , 2008, Science.

[56]  R. Houghton.  Balancing the Global Carbon Budget , 2007 .

[57]  R. N. Cerrillo,et al.  Estimation of above-ground biomass in shrubland ecosystems of southern Spain , 2006 .

[58]  Tatiana Rasteiro Coelho.  13C NMR tracers in neurochemistry: implications for molecular imaging. , 2006 .

[59]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[60]  T. Dawson,et al.  Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol , 2005 .

[61]  B. Prenzel.  Remote sensing-based quantification of land-cover and land-use change for planning , 2004 .

[62]  R. Pongrácz,et al.  Analysis of land-use/land-cover change in the Carpathian region based on remote sensing techniques , 2003 .

[63]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[64]  R. Gifford,et al.  Soil carbon stocks and land use change: a meta analysis , 2002 .

[65]  N. H. Ravindranath,et al.  Land Use, Land-Use Change, and Forestry: A Special Report of the Intergovernmental Panel on Climate Change , 2000 .

[66]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[67]  Jiancheng Shi,et al.  The effect of topography on SAR calibration , 1993, IEEE Trans. Geosci. Remote. Sens..

[68]  Jakob J. van Zyl,et al.  The effect of topography on radar scattering from vegetated areas , 1992, IEEE Trans. Geosci. Remote. Sens..

[69]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[70]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[71]  K. Esbensen,et al.  Principal component analysis , 1987 .

[72]  D. Timothy,et al.  Quantifying aboveground biomass in African environments : A review of the trade-offs between sensor estimation accuracy and costs , 2016 .

[73]  M. A. A. P. D. Carvalho,et al.  Vegetation recovery after a large forest fire in the Ecological Park of Funchal (Madeira Island, Portugal). , 2014 .

[74]  Anthony M. Filippi,et al.  stimation of floodplain aboveground biomass using multispectral emote sensing and nonparametric modeling , 2014 .

[75]  A. Shariff,et al.  Monitoring rangeland ground cover vegetation using multitemporal MODIS data , 2012, Arabian Journal of Geosciences.

[76]  N. Stern.  The Economics of Climate Change , 2007 .

[77]  N. H. Ravindranath,et al.  2006 IPCC Guidelines for National Greenhouse Gas Inventories , 2006 .

[78]  Millenium Ecosystem Assessment.  Ecosystems and human well-being: synthesis , 2005 .

[79]  K. Emery,et al.  The Geology of the Atlantic Ocean , 1967 .