The contribution of remote sensing to the assessment of drought effects in forest ecosystems

Due to their synoptic and monitoring capacities, Earth observation satellites could prove useful for the assessment and evaluation of drought effects in forest ecosystems. The objectives of this article are: to briefly review the existing sources of remote sensing data and their potential to detect drought damage; to review the remote sensing applications and studies carried out during the last two decades aiming at detecting and quantifying disturbances caused by various stress factors, and especially those causing effects similar to drought; to explore the possibility to use some of the different available systems for setting up a strategy more adapted to monitoring of drought effects in forests.

[1]  A. Huete,et al.  A review of vegetation indices , 1995 .

[2]  Alexander Baklanov,et al.  Monitoring of forest damage in the Kola Peninsula, Northern Russia due to smelting industry , 1999 .

[3]  T. M. Bezemer,et al.  Root herbivore effects on above-ground herbivore, parasitoid and hyperparasitoid performance via changes in plant quality , 2005 .

[4]  Dar A. Roberts,et al.  Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index , 2003 .

[5]  P. Holmgren,et al.  Satellite remote sensing for forestry planning—A review , 1998 .

[6]  Josée Lévesque,et al.  Airborne digital camera image semivariance for evaluation of forest structural damage at an acid mine site , 1999 .

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

[8]  Jesús San-Miguel-Ayanz,et al.  Evaluation of RADARSAT-1 Data for Identification of Burnt Areas in Southern Europe. , 2004 .

[9]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[10]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

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

[12]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[13]  A. Vidal,et al.  Evaluation of a temporal fire risk index in mediterranean forests from NOAA thermal IR , 1994 .

[14]  Yann Kerr,et al.  IRSUTE: A Minisatellite Project for Land Surface Heat Flux Estimation from Field to Regional Scale , 1999 .

[15]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[16]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[17]  Jesús San-Miguel-Ayanz,et al.  Identification of burnt areas in Mediterranean forest environments from ERS-2 SAR time series , 2004 .

[18]  Christine Farcy,et al.  European long-term research for sustainable forestry : experimental and monitoring assets at the ecosystem and landscape level : Part 2 : ENFORS field facilities , 2005 .

[19]  W. G. Rees,et al.  Detecting Pollution Damage to Forests in the Kola Peninsula Using the ERS SAR , 2001 .

[20]  Guiping Yu,et al.  A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties , 2000, Plant and Soil.

[21]  D. Guyon,et al.  Utilisation des donnees du moyen infrarouge de Landsat Thematic Mapper pour la mise en evidence des coupes rases sur le Massif Forestier Landais , 1996 .

[22]  Alejandro C. Frery,et al.  Exploratory study of the relationship between tropical forest regeneration stages and SIR-C L and C data , 1997 .

[23]  Yann Kerr,et al.  Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm , 1992 .

[24]  Christine Farcy European long-term research for sustainable forestry : experimental and monitoring assets at the ecosystem and landscape level. : Part 1 , 2005 .

[25]  Pablo J. Zarco-Tejada,et al.  Hyperspectral Remote Sensing of Forest Condition: Estimating Chlorophyll Content in Tolerant Hardwoods , 2003, Forest Science.

[26]  Jean-Pierre Wigneron,et al.  An interactive vegetation SVAT model tested against data from six contrasting sites , 1998 .

[27]  Marta Chiesi,et al.  Estimation of Mediterranean forest transpiration and photosynthesis through the use of an ecosystem simulation model driven by remotely sensed data , 2004 .

[28]  F. Maselli Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data , 2004 .

[29]  A. Jolly Estimation par télédétection satellitaire de la récolte annuelle en bois dans la futaie pure de pin maritime du massif des Landes de Gascogne : apports pour la prévision de la ressource forestière , 1993 .

[30]  W. Liu,et al.  Monitoring regional drought using the Vegetation Condition Index , 1996 .

[31]  D. T. Lindgren Land use planning and remote sensing , 1984 .

[32]  Seppo Nevalainen,et al.  Detection of dead or defoliated spruces using digital aerial data , 2002 .

[33]  R. Koster,et al.  Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation , 2004 .

[34]  A.Vidal Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data , 1991 .

[35]  Kenneth J. Ranson,et al.  Disturbance recognition in the boreal forest using radar and Landsat-7 , 2003 .

[36]  N. Gobron,et al.  The MERIS Global Vegetation Index (MGVI): Description and preliminary application , 1999 .

[37]  W. M. Ciesla,et al.  Interpretation of SPOT-1 color composites for mapping defoliation of hardwood forests by gypsy moth , 1989 .

[38]  Eric S. Kasischke,et al.  Observations of variations in ERS-1 SAR image intensity associated with forest fires in Alaska , 1994, IEEE Trans. Geosci. Remote. Sens..

[39]  N. Gobron,et al.  The state of vegetation in Europe following the 2003 drought , 2005 .

[40]  P. Maisongrande,et al.  Stratified analysis of satellite imagery of SW Europe during summer 2003: the differential response of vegetation classes to increased water deficit , 2005 .

[41]  F. Kogan Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data , 1995 .

[42]  Z. Bochenek,et al.  Deterioration of forests in the Sudety Mountains, Poland, detected on satellite images , 1997 .

[43]  Benoît Duchemin,et al.  Monitoring Phenological Key Stages and Cycle Duration of Temperate Deciduous Forest Ecosystems with NOAA/AVHRR Data , 1999 .

[44]  James E. McMurtrey,et al.  Temporal relationships between spectral response and agronomic variables of a corn canopy , 1981 .

[45]  William D. Bowman,et al.  The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves , 1989 .

[46]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[47]  R. Myneni,et al.  Climate‐related vegetation characteristics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index and normalized difference vegetation index , 2004 .

[48]  N. Coops,et al.  Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations , 2004 .

[49]  Christopher P. Quine,et al.  An investigation of the potential of digital photogrammetry to provide measurements of forest characteristics and abiotic damage , 2000 .

[50]  S. López,et al.  An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain , 1991 .

[51]  P. Berbigier,et al.  MuSICA, a CO2, water and energy multilayer, multileaf pine forest model: evaluation from hourly to yearly time scales and sensitivity analysis , 2003 .

[52]  B. Duchemin,et al.  Potential and limits of NOAA-AVHRR temporal composite data for phenology and water stress monitoring of temperate forest ecosystems , 1999 .

[53]  Andrea Vannini,et al.  Interactive effects of drought and pathogens in forest trees , 2006 .

[54]  Nadine Gobron,et al.  A global vegetation index for SeaWiFS: Design and applications , 2001 .

[55]  F. M. Danson,et al.  Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level , 2004 .

[56]  L. Ji,et al.  Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices , 2003 .

[57]  Eric S. Kasischke,et al.  Initial observations of Radarsat imagery at fire-disturbed sites in interior Alaska , 1999 .

[58]  Eric S. Kasischke,et al.  Mapping fire scars in global boreal forests using imaging radar data , 2002 .

[59]  J. Tenhunen,et al.  On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .

[60]  W. Salas,et al.  Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data☆ , 1997 .

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

[62]  Florian Siegert,et al.  Monitoring of deforestation and land use in Indonesia with multitemporal ERS data , 1999 .

[63]  Eric S. Kasischke,et al.  Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest , 1995 .

[64]  N. Breda,et al.  Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences , 2006 .

[65]  J. Wigneron,et al.  Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans , 2003 .

[66]  S. C. Ahearn,et al.  A comparison of the SPOT and Landsat thematic mapper satellite systems for detecting gypsy moth defoliation in Michigan , 1991 .

[67]  G. Dedieu,et al.  Assessing the impacts of the 2003 hot and dry spell with SPOT HRVIR images time series over south‐western France , 2005 .

[68]  J. Lagouarde,et al.  Experimental study of brightness surface temperature angular variations of maritime pine (Pinus pinaster) stands. , 2000 .

[69]  M. Honzak,et al.  Tropical Forest Biomass Density Estimation Using JERS-1 SAR: Seasonal Variation, Confidence Limits, and Application to Image Mosaics , 1998 .

[70]  J.-C. Valette Inflammabilités des espèces forestières méditerranéennes. Conséquences sur la combustibilité des formations forestières , 1990 .

[71]  P. Ciais,et al.  Europe-wide reduction in primary productivity caused by the heat and drought in 2003 , 2005, Nature.

[72]  Olivier Hagolle,et al.  Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images , 2005 .

[73]  F. Holecz,et al.  Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods , 2003 .

[74]  M. Dobson,et al.  The use of Imaging radars for ecological applications : A review , 1997 .

[75]  P. Couteron,et al.  Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images , 2005 .

[76]  William J. Ripple,et al.  Spectral reflectance relationships to leaf water stress , 1986 .

[77]  V. Wolters,et al.  Impact of summer drought on forest biodiversity: what do we know? , 2006 .

[78]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[79]  S Durrieu,et al.  Méthode de comparaison d'images satellitaires pour la détection des changements en milieu forestier. Application aux monts de Lacaune (Tarn, France) , 1994 .

[80]  Hans Tømmervik,et al.  Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data , 2003 .