Hyperspectral modeling of ecological indicators – A new approach for monitoring former military training areas

Abstract Military areas are valuable habitats and refuges for rare and endangered plants and animals. We developed a new approach applying innovative methods of hyperspectral remote sensing to bridge the existing gap between remote sensing technology and the demands of the nature conservation community. Remote sensing has already proven to be a valuable monitoring instrument. However, the approaches lack the consideration of the demands of applied nature conservation which includes the legal demands of the EU Habitat Directive. Following the idea of the Vital Signs Monitoring in the USA, we identified a subset of the highest priority monitoring indicators for our study area. We analyzed continuous spectral response curves and tested the measurability of N  = 19 indicators on the basis of complexity levels aggregated from extensive vegetation assemblages. The spectral differentiability for the floristic as well as faunistic indicators revealed values up to 100% accuracy. We point out difficulties when it comes to distinguishing faunistic habitat requirements of several species adapted to dry open landscapes, which in this case results in O verall accuracy of 67, 87–95, and 35% in the error matrix. In summary, we provide an applicable and feasible method to facilitating monitoring military areas by hyperspectral remote sensing in the following.

[1]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[2]  Adrian C. Newton,et al.  Identifying cost-effective indicators to assess the conservation status of forested habitats in Natura 2000 sites , 2008 .

[3]  Hannes Feilhauer,et al.  Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape , 2011 .

[4]  C. Daughtry,et al.  Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index , 2011 .

[5]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[6]  Yeqiao Wang Land-Cover Change and Conservation of Protected Lands in Urban and Suburban Settings , 2011 .

[7]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[8]  Onisimo Mutanga,et al.  Continuum - removed absorption features estimate tropical savanna grass quality in situ , 2003 .

[9]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[10]  A. Gitelson,et al.  Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .

[11]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[12]  Roger N. Clark,et al.  Automatic continuum analysis of reflectance spectra , 1987 .

[13]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[14]  Andri Baltensweiler,et al.  High‐resolution remote sensing data improves models of species richness , 2013 .

[15]  Zheng Cai,et al.  A Study on the Changes of Landscape Pattern of Estuary Wetlands of the Minjiang River , 2006 .

[16]  Sebastian Schmidtlein,et al.  Mapping the floristic continuum : Ordination space position estimated from imaging spectroscopy , 2007 .

[17]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[18]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[19]  Bisun Datt,et al.  A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves , 1999 .

[20]  Nigel M. Trodd Analysis and Representation of Heathland Vegetation from Near-Ground Level Remotely-Sensed Data , 1996 .

[21]  G. Zizka,et al.  Using high-resolution remote sensing data for habitat suitability models of Bromeliaceae in the city of Mérida, Venezuela , 2013 .

[22]  A. Gitelson,et al.  Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves¶ , 2001, Photochemistry and photobiology.

[23]  David G. Havlick Disarming Nature: Converting Military Lands to Wildlife Refuges* , 2011 .

[24]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[25]  M. Oliver,et al.  Satellite data identify decadal trends in the quality of Pygoscelis penguin chick‐rearing habitat , 2012, Global change biology.

[26]  R. D. Johnson,et al.  Using Landsat TM data to estimate carbon release from burned biomass in an Alaskan spruce forest complex , 2000 .

[27]  A. Skidmore,et al.  Spectral discrimination of vegetation types in a coastal wetland , 2003 .

[28]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[29]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[30]  Conghe Song,et al.  Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon , 2006 .

[31]  A. Skidmore,et al.  Exploring spectral discrimination of grass species in African rangelands , 2001 .

[32]  Catherine A. Christen,et al.  The Impact of Landsat Satellite Monitoring on Conservation Biology , 2005, Environmental monitoring and assessment.

[33]  Yichun Xie,et al.  Remote sensing imagery in vegetation mapping: a review , 2008 .

[34]  Pierre-Yves Henry,et al.  Habitat monitoring in Europe: a description of current practices , 2008, Biodiversity and Conservation.

[35]  O. Mutanga,et al.  Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution , 2012 .

[36]  C. Carvell Habitat use and conservation of bumblebees (Bombus spp.) under different grassland management regimes , 2002 .

[37]  Susan L. Ustin,et al.  Habitat suitability modelling of an invasive plant with advanced remote sensing data , 2009 .

[38]  Scott J. Goetz,et al.  Application of remote sensing to parks and protected area monitoring: Introduction to the special issue , 2009 .

[39]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[40]  Godela Rossner,et al.  Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods , 2004 .

[41]  Caspar A. Mücher,et al.  Quantifying structure of Natura 2000 heathland habitats using spectral mixture analysis and segmentation techniques on hyperspectral imagery , 2013 .

[42]  D. Bargiel,et al.  Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes , 2013, Ecol. Informatics.

[43]  Zhong Lu,et al.  Monitoring Natural Hazards in Protected Lands Using Interferometric Synthetic Aperture Radar , 2011 .

[44]  Rama Rao Nidamanuri,et al.  Understanding the Unique Spectral Signature of Winter Rape , 2013, Journal of the Indian Society of Remote Sensing.

[45]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[46]  L. Guanter,et al.  Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data , 2007 .

[47]  Utilization of Remote Sensing Technologies for Matschie’s Tree Kangaroo Conservation and Planning in Papua New Guinea , 2011 .

[48]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[49]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[50]  A. Formaggio,et al.  Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data , 2005 .

[51]  Gerald J. Niemi,et al.  Application of Ecological Indicators , 2004 .

[52]  Steven D. Warren,et al.  Biodiversity and the Heterogeneous Disturbance Regime on Military Training Lands , 2007 .

[53]  B. Rock,et al.  Measurement of leaf relative water content by infrared reflectance , 1987 .

[54]  R. Crabtree,et al.  Monitoring and Modeling Environmental Change in Protected Areas: Integration of Focal Species Populations and Remote Sensing* , 2013 .

[55]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[56]  Birgit Kleinschmit,et al.  Approaches to utilising QuickBird data for the monitoring of NATURA 2000 habitats , 2008 .

[57]  Bing Zhang,et al.  Application of hyperspectral remote sensing for environment monitoring in mining areas , 2012, Environmental Earth Sciences.

[58]  Volker C Radeloff,et al.  Modeling habitat suitability for Greater Rheas based on satellite image texture. , 2008, Ecological applications : a publication of the Ecological Society of America.

[59]  A. Knapp,et al.  MILITARY TRAINING EFFECTS ON TERRESTRIAL AND AQUATIC COMMUNITIES ON A GRASSLAND MILITARY INSTALLATION , 2003 .

[60]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[61]  J. Gross,et al.  Monitoring the condition of natural resources in US national parks , 2009, Environmental monitoring and assessment.

[62]  Paul Aplin,et al.  Remote sensing: ecology , 2005 .

[63]  Toon Spanhove,et al.  Can remote sensing estimate fine-scale quality indicators of natural habitats? , 2012 .

[64]  Caspar A. Mücher,et al.  Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward , 2011 .

[65]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[66]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[67]  Alfred Colpaert,et al.  Reindeer Pasture Biomass Assessment Using Satellite Remote Sensing , 2003 .

[68]  Curtis E. Woodcock,et al.  Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors , 2001 .

[69]  Warren B. Cohen,et al.  Monitoring Landscape Dynamics of National Parks in the Western United States , 2011 .

[70]  G. Agati,et al.  New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. , 2001, Journal of photochemistry and photobiology. B, Biology.

[71]  H. Nagendra,et al.  Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats , 2013 .

[72]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[73]  Yafit Cohen,et al.  SWIR-based spectral indices for assessing nitrogen content in potato fields , 2010 .