Forest health assessment for geo-environmental planning and management in hilltop mining areas using Hyperion and Landsat data

Abstract This work focuses on assessing the health condition of the forest using Hyperspectral and Multispectral satellite imagery and validating it with field spectra data. The field-based spectroradiometer and PCE instrument were used for collection of forest health spectra and measurement of dust accumulation on leaves respectively. In the study area, mining activities have very high potential to induce forestry health and environmental problem which necessitated a proper forest health assessment and its monitoring. The result of the Discrimination analysis (21 spectral wavebands) were used for forest health classification. The result shows that healthy forest parts are found in the upper as well as the lower hilly side of Kiriburu and Meghahatuburu mines. Some healthy pixels are located within 1.5 km from mines because it was situated off the hillside. Furthermore, it also exhibits positive relation amongst different forest health class, distance from mines and foliar dust concentration. Hyperspectral (narrow-bands) Hyperion data used with Vegetation Indices (VIs) model shows better accuracy for forest health assessment (overall accuracy 81.52%, kappa statistic 0.79) than Spectral Angle Mapper (overall accuracy 79.99%, kappa statistic 0.75) as well as Support Vector Machine (overall accuracy 76.53%, kappa statistic 0.71). It was observed that the health assessment accuracy (using SVM algorithm) achieved with Hyperion bands was significantly higher than multispectral (broad-bands) Landsat-OLI data (overall accuracy 67.27%, kappa statistic 0.62). Finally, the forest health assessment results were validated by 60 field sampled spectra data obtained from spectroradiometer. The forest health map obtained provides a guideline for geo-environmental planning and management in mining proximity forest area.

[1]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

[2]  Michael R. Wagner,et al.  Concepts of forest health: Utilitarian and ecosystem perspectives , 1994 .

[3]  Paul J. Curran,et al.  Forest ecosystem simulation modelling: the role of remote sensing , 1999 .

[4]  Tarmo Lipping,et al.  Assesment of ENVI Forest Health Tool in Detection of Dust and Seepage Contaminated Forest Areas , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Gary A. Shaw,et al.  Spectral Imaging for Remote Sensing , 2003 .

[6]  Klaus I. Itten,et al.  Geometric and radiometric correction of TM data of mountainous forested areas , 1993, IEEE Trans. Geosci. Remote. Sens..

[7]  Richard G. Lathrop,et al.  Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery , 2005 .

[8]  A. Lugo,et al.  Climate Change and Forest Disturbances , 2001 .

[9]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[10]  George P. Petropoulos,et al.  Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery , 2012, Comput. Geosci..

[11]  G. Bonan.  Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[12]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[13]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

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

[15]  Lorenzo Bruzzone,et al.  An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection , 1995, IEEE Trans. Geosci. Remote. Sens..

[16]  Wenxiu Gao,et al.  Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods , 2013 .

[17]  Prashanth Reddy Marpu,et al.  Hyperspectral Data Dimensionality Reduction and the Impact of Multi-seasonal Hyperion EO-1 Imagery on Classification Accuracies of Tropical Forest Species , 2014 .

[18]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[19]  Ruiliang Pu,et al.  Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  P. Brando,et al.  Forest health and global change , 2015, Science.

[21]  M. Ashton,et al.  Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .

[22]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

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

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

[25]  C. B. S. Dutt,et al.  Tropical forest resource assessment and monitoring , 2002 .

[26]  R. Infante,et al.  Domestication of the Chilean guava (Ugni molinae Turcz.), a forest understorey shrub, must consider light intensity , 2003 .

[27]  Chaichoke Vaiphasa,et al.  Consideration of smoothing techniques for hyperspectral remote sensing , 2006 .

[28]  Francisca López-Granados,et al.  Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops , 2011, Precision Agriculture.

[29]  M. Boussinesq,et al.  Cytokine response profiles predict species-specific infection patterns in human GI nematodes. , 2004, International journal for parasitology.

[30]  W. Oechel,et al.  Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.

[31]  Dhaval Vyas,et al.  Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[32]  Ruiliang Pu,et al.  Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index , 2003, IEEE Trans. Geosci. Remote. Sens..

[33]  Luis Guanter,et al.  Reduction of Uncorrelated Striping Noise - Applications for Hyperspectral Pushbroom Acquisitions , 2014, Remote. Sens..

[34]  W. R. Windham,et al.  Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.

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

[36]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[37]  Prasad S. Thenkabail,et al.  Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization , 2002 .

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

[39]  Josée Lévesque,et al.  Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health , 2003 .

[40]  Paul Telfer,et al.  Parks and People: Assessing the Human Welfare Effects of Establishing Protected Areas for Biodiversity Conservation , 2006, Conservation biology : the journal of the Society for Conservation Biology.

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

[42]  M. Ostry,et al.  Fungi and diseases - natural components of healthy forests. , 2009 .

[43]  P. K Varshney,et al.  Advanced image processing techniques for remotely sensed hyperspectral data : with 128 figures and 30 tables , 2004 .

[44]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

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

[46]  P. R. Meneses,et al.  Spectral Correlation Mapper ( SCM ) : An Improvement on the Spectral Angle Mapper ( SAM ) , 2000 .

[47]  J. Dungan,et al.  Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .

[48]  Prasad S. Thenkabail,et al.  Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation) , 2016 .

[49]  Gregory Asner,et al.  Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[50]  O. Mutanga,et al.  Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review , 2010, Wetlands Ecology and Management.

[51]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[53]  A. Skidmore,et al.  Tropical mangrove species discrimination using hyperspectral data: A laboratory study , 2005 .

[54]  E. Næsset,et al.  REMOTE SENSING OF FOREST HEALTH , 2022 .

[55]  Jinfei Wang,et al.  Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific , 2005 .

[56]  L. Maclauchlan,et al.  Forest health and climate change: A British Columbia perspective , 2010 .

[57]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[58]  N. Ishitsuka,et al.  Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan , 2001 .

[59]  Hitendra Padalia,et al.  Forest tree species discrimination in western Himalaya using EO-1 Hyperion , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[60]  Ronald Aylmer Sir Fisher,et al.  031: Statistical Tests of Agreement Between Observation and Hypothesis. , 1923 .

[61]  G. Allard,et al.  Climate change impacts on forest health , 2008 .

[62]  Y. Inoue,et al.  Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves , 2004, Photosynthetica.

[63]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[64]  R. Chazdon.  Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands , 2008, Science.

[65]  Paul S. Johnson,et al.  The Ecology and Silviculture of Oaks , 2002 .

[66]  K. Kreutzer,et al.  Deposition and soil leaching in stands of Norway spruce and European Beech: Results from the Höglwald research in comparison with other European case studies , 2002, Plant and Soil.

[67]  D. Smart,et al.  Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards , 2007, American Journal of Enology and Viticulture.

[68]  V. M. Chowdary,et al.  Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas , 2019, Journal of Cleaner Production.

[69]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[70]  K. Pathak,et al.  Urban heat island explored by co-relationship between land surface temperature vs multiple vegetation indices , 2016, Spatial Information Research.

[71]  M. Wingfield,et al.  Planted forest health: The need for a global strategy , 2015, Science.

[72]  A. Gitelson,et al.  Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction , 2002 .

[73]  P. Dhar,et al.  Metabolic Engineering of Biosynthetic Pathway for Production of Renewable Biofuels , 2014, Applied Biochemistry and Biotechnology.

[74]  B. Gao,et al.  Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance , 2002 .

[75]  Wu Ma,et al.  Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images , 2016, Remote. Sens..

[76]  D. Hollinger,et al.  Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements , 2007 .

[77]  S. Franklin.  Remote Sensing for Sustainable Forest Management , 2001 .