Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations.

[1]  Elias Symeonakis,et al.  Remote Sensing Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions , 2022 .

[2]  Susan L. Ustin,et al.  Using hyperspectral remote sensing to detect and quantify southeastern pine senescence effects in red-cockaded woodpecker (Picoides borealis) habitat , 2010 .

[3]  David Niemeijer,et al.  Ecosystems and Human Well-Being: Desertification Synthesis , 2005 .

[4]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  A. Dutkiewicz,et al.  Evaluation and comparison of hyperspectral imagery for mapping surface symptoms of dryland salinity , 2009 .

[6]  Pandi Zdruli,et al.  LAND RESOURCES OF THE MEDITERRANEAN: STATUS, PRESSURES, TRENDS AND IMPACTS ON FUTURE REGIONAL DEVELOPMENT , 2014 .

[7]  T. Simoniello,et al.  Watershed influence on fluvial ecosystems: an integrated methodology for river water quality management , 2009, Environmental monitoring and assessment.

[8]  P. Lagacherie,et al.  Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements , 2008 .

[9]  Robert C. Balling,et al.  Impacts of Land Degradation on Historical Temperature Records from the Sonoran Desert , 1998 .

[10]  Tiziana Simoniello,et al.  A geostatistics-assisted approach to the deterministic approximation of climate data , 2015, Environ. Model. Softw..

[11]  Liviu Theodor Ene,et al.  Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models , 2010 .

[12]  M. Greco,et al.  Desertification risk assessment in southern Mediterranean areas , 2005 .

[13]  H. Liniger,et al.  Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia , 2015 .

[14]  M. Schaepman,et al.  Proxy global assessment of land degradation , 2008 .

[15]  John Wainwright,et al.  Land Degradation in Drylands: Reëvaluating Pattern-Process Interrelationships and the Role of Ecogeomorphology , 2014 .

[16]  M. Sivakumar,et al.  Interactions between climate and desertification , 2007 .

[17]  D. Skole,et al.  Assessment of tropical forest degradation by selective logging and fire using Landsat imagery , 2010 .

[18]  Tiziana Simoniello,et al.  Temporal persistence in vegetation cover changes observed from satellite: Development of an estimation procedure in the test site of the Mediterranean Italy , 2004 .

[19]  Mark A. Friedl,et al.  Drought-induced vegetation stress in southwestern North America , 2010 .

[20]  A. Grainger.  Is Land Degradation Neutrality feasible in dry areas , 2015 .

[21]  J. A. Zinck,et al.  Remote sensing of land degradation: experiences from Latin America and the Caribbean. , 2010, Journal of environmental quality.

[22]  T. Downing,et al.  Global Desertification: Building a Science for Dryland Development , 2007, Science.

[23]  A. Tal.  The implications of environmental trading mechanisms on a future Zero Net Land Degradation protocol , 2015 .

[24]  Joachim Hill,et al.  Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS) , 2014, Remote. Sens..

[25]  Tiziana Simoniello,et al.  Indicators for the estimation of vulnerability to land degradation derived from soil compaction and vegetation cover , 2014 .

[26]  J. Puigdefabregas,et al.  Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function , 2008 .

[27]  S. Kaplan,et al.  Land-use change and land degradation in Turkmenistan in the post-Soviet era , 2014 .

[28]  F. Tateo,et al.  Impact of physico-chemical soil properties on erosion features in the Aliano area (Southern Italy) , 2010 .

[29]  S. Sommer,et al.  Enhancing Remotely Sensed Low Resolution Vegetation Data for Assessing Mediterranean Areas Prone to Land Degradation , 2014 .

[30]  Markus Metz,et al.  Terminal Evaluation of the UNEP/FAO/GEF Project "Land Degradation Assessment in Drylands (LADA)" , 2011 .

[31]  Tiziana Simoniello,et al.  Mapping badland areas using LANDSAT TM/ETM satellite imagery and morphological data , 2009 .

[32]  J. Hill,et al.  Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece , 2008 .

[33]  A. Miller,et al.  Tracking geomorphic signatures of watershed suburbanization with multitemporal LiDAR , 2014 .

[34]  Tiziana Simoniello,et al.  Estimation of vegetation cover resilience from satellite time series , 2008 .

[35]  K. Moffett,et al.  Remote Sens , 2015 .

[36]  Un Environment Evaluation Office.  Mid-term evaluation of the UNEP – FAO GEF project: Land Degradation Assessment in Drylands (LADA) , 2009 .

[37]  Wei Zhou,et al.  Quantitative assessment of the individual contribution of climate and human factors to desertification in northwest China using net primary productivity as an indicator , 2015 .

[38]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[39]  R. Coluzzi,et al.  Land cover changes and forest landscape evolution (1985–2009) in a typical Mediterranean agroforestry system (high Agri Valley) , 2014 .

[40]  J. Hill,et al.  Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment , 2009 .

[41]  Aniruddha Ghosh,et al.  A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Clemente Gallardo,et al.  Sensitivity of the Iberian Peninsula climate to a land degradation , 2003 .

[43]  J. Schoonover,et al.  Nutrient Attenuation in Agricultural Surface Runoff by Riparian Buffer Zones in Southern Illinois, USA , 2005, Agroforestry Systems.

[44]  Marion Stellmes,et al.  Assessment and monitoring of land condition in the Iberian Peninsula, 1989–2000 , 2010 .

[45]  J. Hill,et al.  Long‐Term Observation of Mediterranean Ecosystems with Satellite Remote Sensing , 2005 .

[46]  A. Ferrara,et al.  Methodology for mapping Environmentally Sensitive Areas (ESAs) to desertification , 1999 .

[47]  V. K. Prasad,et al.  Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment , 2012 .

[48]  F. M. Danson,et al.  Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data , 2010 .

[49]  Susan L. Ustin,et al.  Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Ju , 2005 .

[50]  Stefano Amaducci,et al.  Assessing canopy PRI from airborne imagery to map water stress in maize , 2013 .

[51]  D. P. Shrestha,et al.  Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain , 2005 .

[52]  M. Piccarreta,et al.  Implications of decadal changes in precipitation and land use policy to soil erosion in Basilicata, Italy , 2006 .

[53]  J. Romm.  Desertification: The next dust bowl , 2011, Nature.

[54]  P. Tarolli,et al.  The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology , 2008 .

[55]  Steven M. De Jong,et al.  Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[56]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[57]  J. Koch,et al.  Model-based analysis of the environmental impacts of grazing management on Eastern Mediterranean ecosystems in Jordan. , 2013, Journal of environmental management.

[58]  Achim Röder,et al.  Mediterranean desertification and land degradation: Mapping related land use change syndromes based on satellite observations , 2008 .

[59]  Gloria Bordogna,et al.  A flexible multi‐source spatial‐data fusion system for environmental status assessment at continental scale , 2008, Int. J. Geogr. Inf. Sci..

[60]  M. Macchiato,et al.  Integrated Indicators for the Estimation of Vulnerability to Land Degradation , 2013 .

[61]  Tiziana Simoniello,et al.  Multiresolution spatial characterization of land degradation phenomena in southern Italy from 1985 to 1999 using NOAA-AVHRR NDVI data , 2003 .

[62]  Spatial patterns of subsistence extraction of forest products – An indirect approach for estimation of forest degradation in dry forest , 2014 .

[63]  George L. W. Perry,et al.  Recent Dynamics of the Mediterranean Vegetation and Landscape , 2005 .

[64]  T. Vågen,et al.  Landsat-based approaches for mapping of land degradation prevalence and soil functional properties in Ethiopia , 2013 .

[65]  M. Keller,et al.  Selective Logging in the Brazilian Amazon , 2005, Science.

[66]  F. Tateo,et al.  The role of mineralogy, geochemistry and grain size in badland development in Pisticci (Basilicata, southern Italy) , 2007 .

[67]  M. Macchiato,et al.  Inter-calibration of Landsat-TM/ETM scenes in heterogeneous areas , 2008 .

[68]  Holly K. Gibbs,et al.  Mapping the world's degraded lands , 2015 .

[69]  L. Allan James,et al.  Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA , 2007 .

[70]  N. Cressie.  Spatial prediction and ordinary kriging , 1988 .

[71]  J. Puigdefabregas,et al.  A MULTIDISCIPLINARY MODEL FOR ASSESSING DEGRADATION IN MEDITERRANEAN RANGELANDS , 2014 .

[72]  Sofia Bajocco,et al.  The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case Study , 2012, Environmental Management.

[73]  Inbal Becker-Reshef,et al.  Detection and mapping of long-term land degradation using local net production scaling: application to Zimbabwe. , 2009 .

[74]  Guifang Liu,et al.  Recent Observations of Human-induced Asymmetric Effects on Climate in Very High-Altitude Area , 2014, PloS one.

[75]  B. Bove,et al.  Indicatori climatici di desertificazione in Basilicata , 2005 .

[76]  Masahiro Tasumi,et al.  Application of MODIS Land Products to Assessment of Land Degradation of Alpine Rangeland in Northern India with Limited Ground-Based Information , 2014, Remote. Sens..

[77]  Sergio M. Vicente-Serrano,et al.  Aridity influence on vegetation patterns in the middle Ebro Valley (Spain): Evaluation by means of AVHRR images and climate interpolation techniques , 2006 .