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Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

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Abstract

Recognizing the imperative need for biodiversity protection, the convention on biological diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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References

  • Abbas A, Khan S, Hussain N, Hanjra MA, Akbar S (2013) Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Phys Chem Earth Pt A/B/C 55–57:43–52

    Article  Google Scholar 

  • Adam E, Mutanga O, Rugege D (2009) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 18(3):281–296

    Article  Google Scholar 

  • Adamo M, Tarantino C, Kosmidou V et al (2013) Land cover to habitat map translation: disambiguation rules based on earth observation data. Int. geoscience and remote sensing symp, IEEE, Melbourne, pp 3817–3820

  • Adamo M, Tarantino C, Tomaselli V et al (2014) Expert knowledge for translating land cover/use maps to general habitat categories (GHC). Landsc Ecol 29(6):1045–1067

    Article  Google Scholar 

  • AHTEG (2011) Report of the Ad Hoc technical expert group on indicators for the strategic plan for biodiversity 2011–2020. Technical report, Ad Hoc technical expert group on indicators for the strategic plan for biodiversity 2011–2020, High Wycombe, UK

  • Alcantara C, Kuemmerle T, Prishchepov AV, Radeloff VC (2012) Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens Environ 124:334–347

    Article  Google Scholar 

  • Altamirano A, Field R, Cayuela L et al (2010) Woody species diversity in temperate Andean forests: the need for new conservation strategies. Biol Conserv 143(9):2080–2091

    Article  Google Scholar 

  • Arias-González JE, Acosta-González G, Membrillo N, Garza-Pérez JR, Castro-Pérez JM (2011) Predicting spatially explicit coral reef fish abundance, richness and ShannonGWeaver index from habitat characteristics. Biodivers Conserv 21(1):115–130

    Article  Google Scholar 

  • Artigas F, Pechmann IC (2010) Balloon imagery verification of remotely sensed Phragmites australis expansion in an urban estuary of New Jersey, USA. Landsc Urban Plan 95(3):105–112

    Article  Google Scholar 

  • Atkinson PM, Pardo-Igúzquiza E, Chica-Olmo M (2008) Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. IEEE T Geosci Remote 46(2):573–580

    Article  Google Scholar 

  • Ayanu YZ, Conrad C, Nauss T, Wegmann M, Koellner T (2012) Quantifying and mapping ecosystem services supplies and demands: a review of remote sensing applications. Environ Sci Technol 46(16):8529–8541

    Article  CAS  PubMed  Google Scholar 

  • Baldeck CA, Colgan MS, Féret JB et al (2014) Landscape-scale variation in plant community composition of an African savanna from airborne species mapping. Ecol Appl 24(1):84–93

    Article  CAS  PubMed  Google Scholar 

  • Balvanera P, Pfisterer AB, Buchmann N et al (2006) Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol Lett 9(10):1146–1156

    Article  PubMed  Google Scholar 

  • Banskota A, Wynne RH, Johnson P, Emessiene B (2011) Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests. Ann For Sci 68(2):347–356

    Article  Google Scholar 

  • Bechle MJ, Millet DB, Marshall JD (2013) Remote sensing of exposure to NO2: satellite versus ground-based measurement in a large urban area. Atmos Environ 69(2):345–353

    Article  CAS  Google Scholar 

  • Bejarano S, Mumby PJ, Sotheran I (2010) Predicting structural complexity of reefs and fish abundance using acoustic remote sensing (RoxAnn). Mar Biol 158(3):489–504

    Article  Google Scholar 

  • Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377

    Article  PubMed Central  PubMed  Google Scholar 

  • Belluco E, Camuffo M, Ferrari S et al (2006) Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sens Environ 105(1):54–67

    Article  Google Scholar 

  • Berberoglu S, Akin A (2009) Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. Int J Appl Earth Obs 11(1):46–53

    Article  Google Scholar 

  • Bergen KM, Gilboy AM, Brown DG (2007) Multi-dimensional vegetation structure in modeling avian habitat. Ecol Inf 2(1):9–22

    Article  Google Scholar 

  • Bergen KM, Goetz SJ, Dubayah RO et al (2009) Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions. J Geophys Res 114:G00E06

  • BIP (2010) Biodiversity indicators and the 2010 Target: experiences and lessons learnt from the 2010 Biodiversity Indicators Partnership. Technical report 53. Secretariat of the Convention on Biological Diversity, Montreal

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65(1):2–16

    Article  Google Scholar 

  • Blaschke T, Lang S, Hay GJ (eds) (2008) Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer, Berlin Heidelberg

    Google Scholar 

  • Blonda P, Lucas R, Inglada J et al (2013) Copernicus Biodiversity Monitoring Services: The FP7 SPACE projects perspective. White Paper. URL http://www.biosos.eu/publ/White_Paper_Biodiversity_Monitoring_BIOSOS_MSMONINA.pdf

  • Bock M, Xofis P, Mitchley J, Rossner G, Wissen M (2005) Object-oriented methods for habitat mapping at multiple scales—case studies from Northern Germany and Wye Downs, UK. J Nat Conserv 13(2–3):75–89

    Article  Google Scholar 

  • Boyd DS, Sanchez-Hernandez C, Foody GM (2006) Mapping a specific class for priority habitats monitoring from satellite sensor data. Int J Remote Sens 27(13):2631–2644

    Article  Google Scholar 

  • Brooks TM, Mittermeier RA, da Fonseca GAB, Gerlach J, Hoffmann M (2006) Global biodiversity conservation priorities. Science 313:58–61

    Article  CAS  PubMed  Google Scholar 

  • Brzank A, Heipke C, Goepfert J (2008a) Morphologic change detection in the Wadden Sea from lidar data. International archives of the photogrammetry. Remote sensing and spatial Information sciences, ISPRS, Beijing, pp 647–652

  • Brzank A, Heipke C, Goepfert J, Soergel U (2008b) Aspects of generating precise digital terrain models in the Wadden Sea from lidarGwater classification and structure line extraction. ISPRS J Photogramm Remote Sens 63(5):510–528

    Article  Google Scholar 

  • Buchanan G, Pearce-Higgins J, Grant M, Robertson D, Waterhouse T (2005) Characterization of moorland vegetation and the prediction of bird abundance using remote sensing. J Biogeogr 32(4):697–707

    Article  Google Scholar 

  • Buchanan GM, Nelson A, Mayaux P, Hartley A, Donald PF (2009) Delivering a global, terrestrial, biodiversity observation system through remote sensing. Conserv Biol 23(2):499–502

    Article  PubMed  Google Scholar 

  • Butchart SHM, Walpole M, Collen B et al (2010) Global biodiversity: indicators of recent declines. Science 328(5982):1164–1168

    Article  CAS  PubMed  Google Scholar 

  • Bwangoy JRB, Hansen MC, Roy DP, Grandi GD, Justice CO (2010) Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens Environ 114(1):73–86

    Article  Google Scholar 

  • Cardinale BJ, Duffy JE, Gonzalez A et al (2012) Biodiversity loss and its impact on humanity. Nature 486:59–67

    Article  CAS  PubMed  Google Scholar 

  • CBD (2010) Report of the tenth meeting of the conference of the parties to the convention on biological diversity. Technical report, convention on biological diversity, Nagoya, Japan

    Google Scholar 

  • CBD (2012) Report of the eleventh meeting of the conference of the parties to the convention on biological diversity. Technical report, convention on biological diversity, Hyderabad, India

  • Chan JCW, Beckers P, Spanhove T, Vanden Borre J (2012) An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. Int J Appl Earth Obs 18:13–22

    Article  Google Scholar 

  • Chen P, Haboudane D, Tremblay N et al (2010) New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens Environ 114(9):1987–1997

    Article  Google Scholar 

  • Chen S, Fang L, Zhang L, Huang W (2009) Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary a case study in Modaomen water way, China. Estuar Coast Shelf S 82(1):119–127

    Article  CAS  Google Scholar 

  • Chen Z, Muller-Karger FE, Hu C (2007) Remote sensing of water clarity in Tampa Bay. Remote Sens Environ 109(2):249–259

    Article  Google Scholar 

  • Chi M, Feng R, Bruzzone L (2008) Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Adv Space Res 41(11):1793–1799

    Article  Google Scholar 

  • Cho MA, Skidmore AK, Sobhan I (2009) Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery. Int J Appl Earth Obs 11(3):201–211

  • Cho MA, Debba P, Mutanga O et al (2012) Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. Int J Appl Earth Obs 16:85–93

    Article  Google Scholar 

  • Clark ML, Roberts DA (2012) Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sens 4(12):1820–1855

    Article  Google Scholar 

  • Clawges R, Vierling K, Vierling L, Rowell E (2008) The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sens Environ 112(5):2064–2073

    Article  Google Scholar 

  • Colgan M, Baldeck C, Féret JB, Asner G (2012) Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data. Remote Sens 4(12):3462–3480

    Article  Google Scholar 

  • Coops NC, Waring RH, Wulder MA, White JC (2009) Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data. Remote Sens Environ 113(5):1058–1066

    Article  Google Scholar 

  • Cornforth W, Fatoyinbo T, Freemantle T, Pettorelli N (2013) Advanced land observing satellite phased array type L-band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remote Sens 5(1):224–237

    Article  Google Scholar 

  • Costanza R, de Groot R, Sutton P et al (2014) Changes in the global value of ecosystem services. Global Environ Change 26:152–158

    Article  Google Scholar 

  • Debeurs K, Townsend P (2008) Estimating the effect of gypsy moth defoliation using MODIS. Remote Sens Environ 112(10):3983–3990

    Article  Google Scholar 

  • Delalieux S, Somers B, Haest B et al (2012) Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers. Remote Sens Environ 126:222–231

    Article  Google Scholar 

  • Demir B, Bovolo F, Bruzzone L (2013) Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE T Geosci Remote 51(1):300–312

    Article  Google Scholar 

  • Dente L, Satalino G, Mattia F, Rinaldi M (2008) Assimilation of leaf area index derived from ASAR and MERIS data into CERES-wheat model to map wheat yield. Remote Sens Environ 112(4):1395–1407

    Article  Google Scholar 

  • Dorigo W, Lucieer A, Podobnikar T, Čarni A (2012) Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. Int J Appl Earth Obs 19:185–195

    Article  Google Scholar 

  • Dubinin M, Potapov P, Lushchekina A, Radeloff VC (2010) Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing. Remote Sens Environ 114(8):1638–1648

    Article  Google Scholar 

  • Duelli P, Obrist MK (2003) Biodiversity indicators: the choice of values and measures. Agric Ecosyst Environ 98(1–3):87–98

    Article  Google Scholar 

  • Duncanson L, Niemann K, Wulder M (2010) Estimating forest canopy height and terrain relief from GLAS waveform metrics. Remote Sens Environ 114(1):138–154

    Article  Google Scholar 

  • Duro DC, Coops NC, Wulder MA, Han T (2007) Development of a large area biodiversity monitoring system driven by remote sensing. Prog Phys Geogr 31(3):235–260

    Article  Google Scholar 

  • Duro DC, Girard J, King DJ et al (2014) Predicting species diversity in agricultural environments using Landsat TM imagery. Remote Sens Environ 144:214–225

    Article  Google Scholar 

  • EEA (2007) Halting the loss of biodiversity by 2010: proposal for a first set of indicators to monitor progress in europe. Technical report. 11, European Environment Agency, Copenhagen

  • EEA (2012a) The impacts of invasive alien species in europe. Technical report 16, European Environment Agency, Copenhagen

  • EEA (2012b) Streamlining european biodiversity indicators 2020: building a future on lessons learnt from the sebi 2010 process. Technical report 11, European Environment Agency, Copenhagen

  • Evans TL, Costa M, Telmer K, Silva TSF (2010) Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE J Sel Top Appl 3(4):560–575

    Google Scholar 

  • Eysn L, Hollaus M, Schadauer K, Pfeifer N (2012) Forest delineation based on airborne LIDAR data. Remote Sens 4(12):762–783

    Article  Google Scholar 

  • Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol S 34(1):487–515

    Article  Google Scholar 

  • Fang H, Liang S, Hoogenboom G (2011) Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. Int J Remote Sens 32(4):1039–1065

    Article  Google Scholar 

  • Feld CK, da Silva PM, Sousa JP et al (2009) Indicators of biodiversity and ecosystem services: a synthesis across ecosystems and spatial scales. Oikos 118(12):1862–1871

    Article  Google Scholar 

  • Féret JB, Asner GP (2013) Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE T Geosci Remote 51(1):73–84

    Article  Google Scholar 

  • Fingas M, Brown C (2014) Review of oil spill remote sensing. Mar Pollut Bull 83(1):9–23

    Article  CAS  PubMed  Google Scholar 

  • Flanders D, Hall-Beyer M, Pereverzoff J (2003) Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Can J Remote Sens 29(4):441–452

    Article  Google Scholar 

  • Forzieri G, Tanteri L, Moser G, Catani F (2013) Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies. Int J Appl Earth Obs 23:313–323

    Article  Google Scholar 

  • Franke J, Navratil P, Keuck V, Peterson K, Siegert F (2012) Monitoring fire and selective logging activities in tropical peat swamp forests. IEEE J Sel Top Appl 5(6):1811–1820

    Google Scholar 

  • Fretwell PT, Trathan PN (2009) Penguins from space: faecal stains reveal the location of emperor penguin colonies. Global Ecol Biogeogr 18(5):543–552

    Article  Google Scholar 

  • Fretwell PT, Larue MA, Morin P et al (2012) An emperor penguin population estimate: the first global, synoptic survey of a species from space. PloS One 7(4):e33,751

  • Fretwell PT, Staniland IJ, Forcada J (2014) Whales from space: counting southern right whales by satellite. PloS One 9(2):e88,655

  • Fuchs H, Magdon P, Kleinn C, Flessa H (2009) Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory. Remote Sens Environ 113(3):518–531

    Article  Google Scholar 

  • Fuentes D, Gamon J, Cheng Y et al (2006) Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens Environ 103(3):312–323

    Article  Google Scholar 

  • Gallego J, Bamps C (2008) Using CORINE land cover and the point survey LUCAS for area estimation. Int J Appl Earth Obs 10(4):467–475

    Article  Google Scholar 

  • García M, Riaño D, Chuvieco E, Danson FM (2010) Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ 114(4):816–830

    Article  Google Scholar 

  • García-Gigorro S, Saura S (2005) Forest fragmentation estimated from remotely sensed data: Is comparison across scales possible? For Sci 51(1):51–63

    Google Scholar 

  • Garcia-Pineda O, Zimmer B, Howard M et al (2009) Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA). Can J Remote Sens 35(5):411–421

    Article  Google Scholar 

  • Geo BON (2011) Adequacy of biodiversity observation systems to support the cbd 2020 targets. Technical report, Group on Earth Observations Biodiversity Observation Network, Pretoria, South Africa

  • Ghiyamat A, Shafri HZM, Mahdiraji GA, Shariff ARM, Mansor S (2013) Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember. Int J Appl Earth Obs 23:177–191

    Article  Google Scholar 

  • Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Prog Phys Geog 32(2):203–221

    Article  Google Scholar 

  • Gong Z, Gong H, Zhao W, Li X, Hu Z (2007) Using RS and GIS to monitoring Beijing wetland resources evolution. IEEE international geoscience and remote sensing symposium, IEEE, Barcelona, pp 4596–4599

  • Goodwin BJ (2003) Is landscape connectivity a dependent or independent variable? Landsc Ecol 18(7):687–699

    Article  Google Scholar 

  • Goodwin NR, Collett LJ (2014) Development of an automated method for mapping fire history captured in Landsat TM and ETM+ time series across Queensland, Australia. Remote Sens Environ 148:206–221

    Article  Google Scholar 

  • Gorsevski V, Kasischke E, Dempewolf J, Loboda T, Grossmann F (2012) Analysis of the impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan Uganda border using multitemporal Landsat imagery. Remote Sens Environ 118:10–20

    Article  Google Scholar 

  • Griffiths P, Kuemmerle T, Kennedy RE et al (2012) Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sens Environ 118:199–214

    Article  Google Scholar 

  • Grinand C, Rakotomalala F, Gond V et al (2013) Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sens Environ 139:68–80

    Article  Google Scholar 

  • Guyon D, Guillot M, Vitasse Y et al (2011) Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series. Remote Sens Environ 115(2):615–627

    Article  Google Scholar 

  • Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74

    Article  Google Scholar 

  • Hantson W, Kooistra L, Slim PA (2012) Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs. Appl Veg Sci 15(4):536–547

    Article  Google Scholar 

  • He KS, Rocchini D, Neteler M, Nagendra H (2011) Benefits of hyperspectral remote sensing for tracking plant invasions. Divers Distrib 17(3):381–392

    Article  Google Scholar 

  • Herrmann I, Pimstein A, Karnieli A et al (2011) LAI assessment of wheat and potato crops by VEN\(\mu\)S and Sentinel-2 bands. Remote Sens Environ 115(8):2141–2151

    Article  Google Scholar 

  • Hestir EL, Khanna S, Andrew ME et al (2008) Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sens Environ 112(11):4034–4047

    Article  Google Scholar 

  • Hmimina G, Dufrêne E, Pontailler JY et al (2013) Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: an investigation using ground-based NDVI measurements. Remote Sens Environ 132:145–158

    Article  Google Scholar 

  • Höfle B, Vetter M (2009) Water surface mapping from airborne laser scanning using signal intensity and elevation data. Earth Surf Proc Land 34(12):1635–1649

    Article  Google Scholar 

  • Hou Z, Xu Q, Tokola T (2011) Use of ALS, Airborne CIR and ALOS AVNIR-2 data for estimating tropical forest attributes in Lao PDR. ISPRS J Photogramm 66(6):776–786

    Article  Google Scholar 

  • Huang C, Goward SN, Masek JG et al (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114(1):183–198

    Article  Google Scholar 

  • Hufkens K, Friedl M, Sonnentag O et al (2012) Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens Environ 117:307–321

    Article  Google Scholar 

  • Hüttich C, Gessner U, Herold M et al (2009) On the suitability of MODIS time series metrics to map vegetation types in dry savanna ecosystems: a case study in the Kalahari of NE Namibia. Remote Sens 1(4):620–643

    Article  Google Scholar 

  • Hyyppä J, Yu X, Hyyppä H et al (2012) Advances in forest inventory using airborne laser scanning. Remote Sens 4(12):1190–1207

    Article  Google Scholar 

  • Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens 4(12):2661–2693

    Article  Google Scholar 

  • Jain M, Mondal P, DeFries RS, Small C, Galford GL (2013) Mapping cropping intensity of smallholder farms: a comparison of methods using multiple sensors. Remote Sens Environ 134:210–223

    Article  Google Scholar 

  • Jaskierniak D, Lane PN, Robinson A, Lucieer A (2011) Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions. Remote Sens Environ 115(2):573–585

    Article  Google Scholar 

  • Jenkins RB, Frazier PS (2010) High-resolution remote sensing of upland swamp boundaries and vegetation for baseline mapping and monitoring. Wetlands 30(3):531–540

    Article  Google Scholar 

  • Jiang H, Strittholt JR, Frost PA, Slosser NC (2004) The classification of late seral forests in the Pacific Northwest, USA using Landsat ETM+ imagery. Remote Sens Environ 91(3–4):320–331

    Article  Google Scholar 

  • Jonsson BG, Jonsell M (1999) Exploring potential biodiversity indicators in boreal forests. Biodivers Conserv 8(10):1417–1433

    Article  Google Scholar 

  • Joshi C, de Leeuw J, van Duren IC (2004) Remote sensing and GIS applications for mapping and spatial modelling of invasive species. In: International archives of the photogrammetry, remote sensing and spatial Information sciences, vol XXXV-B7. ISPRS, Istanbul, pp 669–677

  • Kabbara N, Benkhelil J, Awad M, Barale V (2008) Monitoring water quality in the coastal area of Tripoli (Lebanon) using high-resolution satellite data. ISPRS J Photogramm 63(5):488–495

    Article  Google Scholar 

  • Kachelriess D, Wegmann M, Gollock M, Pettorelli N (2014) The application of remote sensing for marine protected area management. Ecol Indic 36:169–177

    Article  Google Scholar 

  • Kati V, Devillers P, Dufrêne M et al (2004) Testing the value of six taxonomic groups as biodiversity indicators at a local scale. Conserv Biol 18(3):667–675

    Article  Google Scholar 

  • Kayitakire F, Hamel C, Defourny P (2006) Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens Environ 102(3–4):390–401

    Article  Google Scholar 

  • Kefauver SC, Peñuelas J, Ustin S (2013) Using topographic and remotely sensed variables to assess ozone injury to conifers in the Sierra Nevada (USA) and Catalonia (Spain). Remote Sens Environ 139:138–148

    Article  Google Scholar 

  • Kindlmann P, Burel F (2008) Connectivity measures: a review. Landsc Ecol 23(8):879–890

    Google Scholar 

  • Kobler A, Džeroski S, Keramitsoglou I (2006) Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image. Ecol Model 191(1):83–95

    Article  Google Scholar 

  • Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm 65(6):581–590

    Article  Google Scholar 

  • Kokaly RF, Couvillion BR, Holloway JM et al (2013) Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes. Remote Sens Environ 129:210–230

    Article  Google Scholar 

  • Koy K, McShea WJ, Leimgruber P, Haack BN, Aung M (2005) Percentage canopy cover—using Landsat imagery to delineate habitat for Myanmar’s endangered Eld’s deer (Cervus eldi). Anim Conserv 8(3):289–296

    Article  Google Scholar 

  • Kronseder K, Ballhorn U, Böhm V, Siegert F (2012) Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. Int J Appl Earth Obs 18:37–48

    Article  Google Scholar 

  • Kuemmerle T, Chaskovskyy O, Knorn J et al (2009) Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sens Environ 113(6):1194–1207

    Article  Google Scholar 

  • Kugler F, Schulze D, Hajnsek I, Pretzsch H, Papathanassiou KP (2014) TanDEM-X Pol-InSAR performance for forest height estimation. IEEE T Geosci Remote 52(10):6404–6422

    Article  Google Scholar 

  • Kumar S, Simonson SE, Stohlgren TJ (2009) Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA. Biodivers Conserv 18(3):739–763

    Article  Google Scholar 

  • Kumar T, Patnaik C (2013) Discrimination of mangrove forests and characterization of adjoining land cover classes using temporal C-band synthetic aperture radar data: a case study of Sundarbans. Int J Appl Earth Obs 23:119–131

    Article  Google Scholar 

  • Laba M, Downs R, Smith S et al (2008) Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using Quickbird satellite imagery. Remote Sens Environ 112(1):286–300

    Article  Google Scholar 

  • Lang MW, McCarty GW (2009) Lidar intensity for improved detection of inundation below the forest canopy. Wetlands 29(4):1166–1178

    Article  Google Scholar 

  • Lang MW, Kasischke ES, Prince SD, Pittman KW (2008) Assessment of C-band synthetic aperture radar data for mapping and monitoring coastal plain forested wetlands in the Mid-Atlantic Region, USA. Remote Sens Environ 112(11):4120–4130

    Article  Google Scholar 

  • Langner A, Samejima H, Ong RC, Titin J, Kitayama K (2012) Integration of carbon conservation into sustainable forest management using high resolution satellite imagery: a case study in Sabah, Malaysian Borneo. Int J Appl Earth Obs 18:305–312

    Article  Google Scholar 

  • Latifi H, Nothdurft A, Koch B (2010) Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry 83(4):395–407

    Article  Google Scholar 

  • Laurance WF, Useche DC, Rendeiro J et al (2012) Averting biodiversity collapse in tropical forest protected areas. Nature 489(7415):290–294

    Article  CAS  PubMed  Google Scholar 

  • Lechner A, Fletcher A, Johansen K, Erskine P (2012) Characterising upland swamps using object-based classification methods and hyper-spatial resolution imagery derived from an unmanned aerial vehicle. In: ISPRS annals of photogrammetry, remote sensing and spatial Information sciences, vol I–4. ISPRS, Melbourne, pp 101–106

  • Leifer I, Lehr WJ, Simecek-Beatty D et al (2012) State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill. Remote Sens Environ 124:185–209

    Article  Google Scholar 

  • Liesenberg V, Gloaguen R (2013) Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil. Int J Appl Earth Obs 21:122–135

    Article  Google Scholar 

  • Lindberg E, Hollaus M (2012) Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest. Remote Sens 4(12):1004–1023

    Article  Google Scholar 

  • Lindenmayer DB, Margules CR, Botkin DB (2000) Indicators of biodiversity for ecologically sustainable forest management. Conserv Biol 14(4):941–950

    Article  Google Scholar 

  • Liu S, Dong Y, Deng L et al (2014) Forest fragmentation and landscape connectivity change associated with road network extension and city expansion: A case study in the Lancang River Valley. Ecol Indic 36:160–168

    Article  Google Scholar 

  • Longépé N, Rakwatin P, Isoguchi O, Shimada M (2011) Assessment of ALOS PALSAR 50 m orthorectified FBD data for regional land cover classification by support vector machines. IEEE T Geosci Remote 49(6):2135–2150

    Article  Google Scholar 

  • Lucas R, Blonda P, Bunting P et al (2015) The earth observation data for habitat monitoring (EODHaM) system. Int J Appl Earth Obs 37:17–28

    Article  Google Scholar 

  • Lucas RM, Medcalf K, Brown A et al (2011) Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data. ISPRS J Photogramm 66(1):81–102

    Article  Google Scholar 

  • Lück-Vogel M, OFarrell PJ, Roberts W (2013) Remote sensing based ecosystem state assessment in the Sandveld Region, South Africa. Ecol Indic 33:60–70

    Article  Google Scholar 

  • Lynch J, Maslin M, Balzter H, Sweeting M (2013) Choose satellites to monitor deforestation. Nature 496(7445):293–294

    Article  CAS  PubMed  Google Scholar 

  • Maes J, Teller A, Erhard M et al (2013) Mapping and assessment of ecosystems and their services: an analytical framework for ecosystem assessments under action 5 of the EU biodiversity strategy to 2020. Publications office of the European Union, Luxembourg, Technical report

  • Maghsoudi Y, Collins M, Leckie DG (2012) Polarimetric classification of boreal forest using nonparametric feature selection and multiple classifiers. Int J Appl Earth Obs 19:139–150

    Article  Google Scholar 

  • Mairota P, Cafarelli B, Labadessa R et al (2015) Very high resolution earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas. Int J Appl Earth Obs 37:100–105

    Article  Google Scholar 

  • Manakos I, Schneider T, Ammer U (2000) A comparison between the ISODATA and the eCognition classification methods on basis of field data. In: International archives of photogrammetry and remote sensing, vol XXXIII (Supplement B7). ISPRS, Amsterdam, pp 133–139

  • Martín-Martín C, Bunce RG, Saura S, Elena-Rosselló R (2013) Changes and interactions between forest landscape connectivity and burnt area in Spain. Ecol Indic 33:129–138

    Article  Google Scholar 

  • Martínez-López J, Carreño M, Palazón-Ferrando J, Martínez-Fernández J, Esteve M (2014) Remote sensing of plant communities as a tool for assessing the condition of semiarid Mediterranean saline wetlands in agricultural catchments. Int J Appl Earth Obs 26:193–204

    Article  Google Scholar 

  • McDermid GJ, Franklin SE, LeDrew EF (2005) Remote sensing for large-area habitat mapping. Prog Phys Geog 29(4):449–474

    Article  Google Scholar 

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program. University of Massachusetts, Amherst. Available at http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • Melin M, Packalén P, Matala J, Mehtätalo L, Pusenius J (2013) Assessing and modeling moose (Alces alces) habitats with airborne laser scanning data. Int J Appl Earth Obs 23:389–396

    Article  Google Scholar 

  • Mera D, Cotos JM, Varela-Pet J, Garcia-Pineda O (2012) Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula. Mar Pollut Bull 64(10):2090–2096

    Article  CAS  PubMed  Google Scholar 

  • Miao X, Gong P, Swope S et al (2006) Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear spectral mixture models. Remote Sens Environ 101(3):329–341

    Article  Google Scholar 

  • Moriondo M, Maselli F, Bindi M (2007) A simple model of regional wheat yield based on NDVI data. Eur J Agron 26(3):266–274

    Article  Google Scholar 

  • Motohka T, Shimada M, Uryu Y, Setiabudi B (2014) Using time series PALSAR gamma nought mosaics for automatic detection of tropical deforestation: a test study in Riau, Indonesia. Remote Sens Environ. In press

  • Muad AM, Foody GM (2012) Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution. Int J Appl Earth Obs 15:79–91

    Article  Google Scholar 

  • Müller J, Brandl R (2009) Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. J Appl Ecol 46(4):897–905

    Article  Google Scholar 

  • Mwita E, Menz G, Misana S et al (2013) Mapping small wetlands of Kenya and Tanzania using remote sensing techniques. Int J Appl Earth Obs 21:173–183

    Article  Google Scholar 

  • Nagendra H (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22(12):2377–2400

    Article  Google Scholar 

  • Nagendra H, Lucas R, Honrado JP et al (2013) Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol Indic 33:45–59

    Article  Google Scholar 

  • Nelson R, Ranson K, Sun G et al (2009) Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sens Environ 113(3):691–701

    Article  Google Scholar 

  • Newman ME, McLaren KP, Wilson BS (2014) Assessing deforestation and fragmentation in a tropical moist forest over 68 years; the impact of roads and legal protection in the Cockpit Country, Jamaica. For Ecol Manag 315:138–152

    Article  Google Scholar 

  • Nichol JE, Sarker MLR (2011) Improved biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE T Geosci Remote 49(3):930–948

    Article  Google Scholar 

  • Olmanson LG, Bauer ME, Brezonik PL (2008) A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sens Environ 112(11):4086–4097

    Article  Google Scholar 

  • Ozdemir I, Karnieli A (2011) Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel. Int J Appl Earth Obs 13(5):701–710

    Article  Google Scholar 

  • Pan Z, Yu J, Huang H et al (2013) Super-resolution based on compressive sensing and structural self-similarity for remote sensing images. IEEE T Geosci Remote 51(9):4864–4876

    Article  Google Scholar 

  • Paneque-Gálvez J, Mas JF, Moré G et al (2013) Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. Int J Appl Earth Obs 23:372–383

    Article  Google Scholar 

  • Patel RC, Joshi MV (2015) Super-resolution of hyperspectral images: Use of optimum wavelet filter coefficients and sparsity regularization. IEEE T Geosci Remote 53(4):1728–1736

    Article  Google Scholar 

  • Pengra BW, Ca Johnston, Loveland TR (2007) Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sens Environ 108(1):74–81

    Article  Google Scholar 

  • Pereira HM, Navarro LM, Martins IS (2012) Global biodiversity change: the bad, the good, and the unknown. Annu Rev Env Resour 37:25–50

    Article  Google Scholar 

  • Pereira HM, Ferrier S, Walters M et al (2013) Essential biodiversity variables. Science 339:277–278

    Article  CAS  PubMed  Google Scholar 

  • Pérez-Luque AJ, Pérez-Pérez R, Bonet-García FJ, Magaña PJ (2015) An ontological system based on MODIS images to assess ecosystem functioning of Natura 2000 habitats: a case study for Quercus pyrenaica forests. Int J Appl Earth Obs 37:142–151

    Article  Google Scholar 

  • Perry EM, Fitzgerald GJ, Nuttall JG et al (2012) Rapid estimation of canopy nitrogen of cereal crops at paddock scale using a canopy chlorophyll content index. Field Crop Res 134:158–164

    Article  Google Scholar 

  • Petrou ZI, Tarantino C, Adamo M, Blonda P, Petrou M (2012) Estimation of vegetation height through satellite image texture analysis. In: International archives of the photogrammetry, remote sensing and spatial Information sciences, Melbourne, vol XXXIX-B8, pp 321–326

  • Petrou ZI, Kosmidou V, Manakos I et al (2014) A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic. Pattern Recognit Lett 48:24–33

    Article  Google Scholar 

  • Pettorelli N, Laurance WF, O’Brien TG et al (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51:839–848

    Article  Google Scholar 

  • Pittiglio C, Skidmore AK, van Gils HAMJ, Prins HH (2012) Identifying transit corridors for elephant using a long time-series. Int J Appl Earth Obs 14(1):61–72

    Article  Google Scholar 

  • Plexida SG, Sfougaris AI, Ispikoudis IP, Papanastasis VP (2014) Selecting landscape metrics as indicators of spatial heterogeneity: a comparison among Greek landscapes. Int J Appl Earth Obs 26:26–35

    Article  Google Scholar 

  • Pontius J, Martin M, Plourde L, Hallett R (2008) Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies. Remote Sens Environ 112(5):2665–2676

    Article  Google Scholar 

  • Popescu SC, Zhao K, Neuenschwander A, Lin C (2011) Satellite lidar vs. small footprint airborne lidar: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens Environ 115(11):2786–2797

    Article  Google Scholar 

  • Price KP, Guo X, Stiles JM (2002) Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas. Int J Remote Sens 23(23):5031–5042

    Article  Google Scholar 

  • Pu R, Bell S, Levy KH, Meyer C (2010) Mapping detailed seagrass habitats using satellite imagery. IEEE Int. Geoscience and Remote Sensing Symposium, Honolulu, Hi, pp 1–4

    Google Scholar 

  • Purkis SJ, Graham NAJ, Riegl BM (2008) Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago). Coral Reefs 27(1):167–178

    Article  Google Scholar 

  • Reiche M, Funk R, Zhang Z et al (2012) Application of satellite remote sensing for mapping wind erosion risk and dust emission-deposition in Inner Mongolia grassland, China. Grassl Sci 58(1):8–19

    Article  Google Scholar 

  • Ren J, Chen Z, Zhou Q, Tang H (2008) Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int J Appl Earth Obs 10(4):403–413

    Article  Google Scholar 

  • Renó VF, Novo EM, Suemitsu C, Rennó CD, Silva TS (2011) Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery. Remote Sens Environ 115(12):3446–3456

    Article  Google Scholar 

  • Ryu Y, Lee G, Jeon S, Song Y, Kimm H (2014) Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors. Remote Sens Environ 149:227–238

    Article  Google Scholar 

  • Sandberg G, Ulander L, Fransson J, Holmgren J, Le Toan T (2011) L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest. Remote Sens Environ 115(11):2874–2886

    Article  Google Scholar 

  • Sano EE, Rosa R, Brito JLS, Ferreira LG (2010) Land cover mapping of the tropical savanna region in Brazil. Environ Monit Assess 166(1–4):113–124

    Article  PubMed  Google Scholar 

  • Saura S, Pascual-Hortal L (2007) A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc Urban Plan 83(2–3):91–103

    Article  Google Scholar 

  • Schmidt A, Soergel U (2013) Monitoring and change detection of Wadden Sea areas using Lidar data. In: International archives of the photogrammetry, remote sensing and spatial information sciences, ISPRS, Antalya, vol XL-7/W2, pp 219–224

  • Scholes RJ, Walters M, Turak E et al (2012) Building a global observing system for biodiversity. Curr Opin Env Sustain 4(1):139–146

    Article  Google Scholar 

  • Schroeder TA, Wulder MA, Healey SP, Moisen GG (2011) Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sens Environ 115(6):1421–1433

    Article  Google Scholar 

  • Schuster C, Ali I, Lohmann P et al (2011) Towards detecting swath events in TerraSAR-X time series to establish Natura 2000 grassland habitat swath management as monitoring parameter. Remote Sens 3(12):1308–1322

    Article  Google Scholar 

  • Secades C, O’Connor B, Brown C, Walpole M (2014) Earth observation for biodiverity monitoring: a review of current approaches and future opportunities for tracking progress towards the Aichi Biodiversity Targets. Technical Report 72, Secretariat of the Convention on Biological Diversity, Montreal

  • Sedano F, Kempeneers P, Miguel JS, Strobl P, Vogt P (2013) Towards a pan-European burnt scar mapping methodology based on single date medium resolution optical remote sensing data. Int J Appl Earth Obs 20:52–59

    Article  Google Scholar 

  • Shahriar Pervez M, Budde M, Rowland J (2014) Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI. Remote Sens Environ 149:155–165

    Article  Google Scholar 

  • Shen Z, Liao Q, Hong Q, Gong Y (2012) An overview of research on agricultural non-point source pollution modelling in China. Sep Purif Technol 84:104–111

    Article  CAS  Google Scholar 

  • Shouse M, Liang L, Fei S (2013) Identification of understory invasive exotic plants with remote sensing in urban forests. Int J Appl Earth Obs 21:525–534

    Article  Google Scholar 

  • Simonson WD, Allen HD, Coomes DA (2013) Remotely sensed indicators of forest conservation status: case study from a Natura 2000 site in southern Portugal. Ecol Indic 24:636–647

    Article  Google Scholar 

  • Somers B, Asner GP (2012) Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sens 4(12):2510–2529

    Article  Google Scholar 

  • Song H, Huang B, Liu Q, Zhang K (2015) Improving the spatial resolution of Landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution. IEEE T Geosci Remote 53(3):1195–1204

    Article  Google Scholar 

  • Soudani K, François C, le Maire G, Le Dantec V, Dufrêne E (2006) Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sens Environ 102(1–2):161–175

    Article  Google Scholar 

  • Strand H, Höft R, Strittholt J et al (2007) Sourcebook on remote sensing and biodiversity indicators. Technical report 32, Secretariat of the Convention on Biological Diversity, Montreal

  • Suarez-Seoane S, Osborne PE, Alonso JC (2002) Large-scale habitat selection by agricultural steppe birds in Spain: identifying species-habitat responses using generalized additive models. J Appl Ecol 39(5):755–771

    Article  Google Scholar 

  • Tang J, Bu K, Yang J, Zhang S, Chang L (2012) Multitemporal analysis of forest fragmentation in the upstream region of the Nenjiang River Basin, Northeast China. Ecol Indic 23:597–607

    Article  Google Scholar 

  • Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer B (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91(3–4):354–376

    Article  Google Scholar 

  • Tilling AK, O’Leary GJ, Ferwerda JG et al (2007) Remote sensing of nitrogen and water stress in wheat. Field Crop Res 104(1–3):77–85

    Article  Google Scholar 

  • Tonolli S, Dalponte M, Neteler M et al (2011) Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps. Remote Sens Environ 115(10):2486–2498

    Article  Google Scholar 

  • Topouzelis K, Psyllos A (2012) Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS J Photogramm 68:135–143

    Article  Google Scholar 

  • Turner W (2013) Satellites: make data freely accessible. Nature 498(7452):37

    Article  CAS  PubMed  Google Scholar 

  • Turner W, Spector S, Gardiner N et al (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18(6):306–314

    Article  Google Scholar 

  • Vaglio Laurin G, Liesenberg V, Chen Q et al (2013) Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. Int J Appl Earth Obs 21:7–16

    Article  Google Scholar 

  • Vanden Borre J, Paelinckx D, Mücher CA et al (2011) Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. J Nat Conserv 19(2):116–125

    Article  Google Scholar 

  • Vastaranta M, Holopainen M, Karjalainen M et al (2014) TerraSAR-X stereo radargrammetry and airborne scanning LiDAR height metrics in imputation of forest aboveground biomass and stem volume. IEEE T Geosci Remote 52(2):1197–1204

    Article  Google Scholar 

  • Vierling KT, Bässler C, Brandl R et al (2011) Spinning a laser web: predicting spider distributions using LiDAR. Ecol Appl 21(2):577–588

    Article  CAS  PubMed  Google Scholar 

  • Virtanen T, Ek M (2014) The fragmented nature of tundra landscape. Int J Appl Earth Obs 27:4–12

    Article  Google Scholar 

  • Vogeler JC, Hudak AT, Vierling LA et al (2014) Terrain and vegetation structural influences on local avian species richness in two mixed-conifer forests. Remote Sens Environ 147:13–22

    Article  Google Scholar 

  • Vyas D, Krishnayya NSR, Manjunath KR, Ray SS, Panigrahy S (2011) Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation. Int J Appl Earth Obs 13(2):228–235

    Article  Google Scholar 

  • Walker WS, Stickler CM, Kellndorfer JM, Kirsch KM, Nepstad DC (2010) Large-area classification and mapping of forest and land cover in the Brazilian Amazon: a comparative analysis of ALOS/PALSAR and Landsat data sources. IEEE J Sel Top Appl 3(4):594–604

    Google Scholar 

  • Walsh SJ, McCleary AL, Mena CF et al (2008) QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management. Remote Sens Environ 112(5):1927–1941

    Article  Google Scholar 

  • Wang X, Wang Q, Wu C et al (2012) A method coupled with remote sensing data to evaluate non-point source pollution in the Xin’anjiang catchment of China. Sci Total Environ 430:132–143

    Article  CAS  PubMed  Google Scholar 

  • Wang X, Huang H, Gong P et al (2014) Forest canopy height extraction in rugged areas with ICESat/GLAS data. IEEE T Geosci Remote 52(8):4650–4657

    Article  Google Scholar 

  • Wang Z, Huang N, Luo L et al (2011) Shrinkage and fragmentation of marshes in the West Songnen Plain, China, from 1954 to 2008 and its possible causes. Int J Appl Earth Obs 13(3):477–486

    Article  Google Scholar 

  • White JC, Gómez C, Wulder MA, Coops NC (2010) Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data. Remote Sens Environ 114(7):1576–1589

    Article  Google Scholar 

  • White K, Pontius J, Schaberg P (2014) Remote sensing of spring phenology in northeastern forests: a comparison of methods, field metrics and sources of uncertainty. Remote Sens Environ 148:97–107

    Article  Google Scholar 

  • Whittle M, Quegan S, Uryu Y, Stüewe M, Yulianto K (2012) Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sens Environ 124:83–98

    Article  Google Scholar 

  • Wijedasa LS, Sloan S, Michelakis DG, Clements GR (2012) Overcoming limitations with Landsat imagery for mapping of peat swamp forests in Sundaland. Remote Sens 4(12):2595–2618

    Article  Google Scholar 

  • Wolter PT, Townsend PA, Sturtevant BR (2009) Estimation of forest structural parameters using 5 and 10-meter SPOT-5 satellite data. Remote Sens Environ 113(9):2019–2036

    Article  Google Scholar 

  • Wu C, Gonsamo A, Gough CM, Chen JM, Xu S (2014) Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sens Environ 147:79–88

    Article  Google Scholar 

  • Wulder MA, White JC, Coops NC, Butson CR (2008) Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sens Environ 112(6):2729–2740

    Article  Google Scholar 

  • Wulder MA, White JC, Nelson RF et al (2012) Lidar sampling for large-area forest characterization: a review. Remote Sens Environ 121:196–209

    Article  Google Scholar 

  • Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1(1):9–23

    Article  Google Scholar 

  • Yan H, Fu Y, Xiao X et al (2009) Modeling gross primary productivity for winter wheatmaize double cropping system using MODIS time series and CO2 eddy flux tower data. Agric Ecosyst Environ 129(4):391–400

    Article  Google Scholar 

  • Yen KW, Lu HJ, Chang Y, Lee MA (2012) Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean. Int J Remote Sens 33(23):7507–7522

    Article  Google Scholar 

  • Zainuddin M, Kiyofuji H, Saitoh K, Saitoh SI (2006) Using multi-sensor satellite remote sensing and catch data to detect ocean hot spots for albacore (Thunnus alalunga) in the northwestern North Pacific. Deep-Sea Res Pt II 53(3–4):419–431

    Article  Google Scholar 

  • Zhao D, Cai Y, Jiang H et al (2011) Estimation of water clarity in Taihu Lake and surrounding rivers using Landsat imagery. Adv Water Resour 34(2):165–173

    Article  Google Scholar 

  • Zhao K, Popescu S (2009) Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sens Environ 113(8):1628–1645

    Article  Google Scholar 

  • Zhong L, Gong P, Biging GS (2014) Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery. Remote Sens Environ 140:1–13

    Article  Google Scholar 

  • Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens Environ 122:75–91

    Article  Google Scholar 

  • Zlinszky A, Mücke W, Lehner H, Briese C, Pfeifer N (2012) Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary. Remote Sens 4(12):1617–1650

    Article  Google Scholar 

  • Zohmann M, Pennerstorfer J, Nopp-Mayr U (2013) Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling. Ecol Model 254:22–32

    Article  Google Scholar 

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  1. Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Zisis I. Petrou & Tania Stathaki

  2. Information Technologies Institute, Centre for Research and Technology Hellas, P.O. Box 60361, 6th km Xarilaou - Thermi, 57001, Thessaloniki, Greece

    Zisis I. Petrou & Ioannis Manakos

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Petrou, Z.I., Manakos, I. & Stathaki, T. Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets. Biodivers Conserv 24, 2333–2363 (2015). https://doi.org/10.1007/s10531-015-0947-z

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