Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches
Subject Areas : Geospatial systems developmentMohsen Mirzaie 1 , Mozhgan Abbasi 2 , Safar Marofi 3 , Eisa Solgi 4 , Roohollah Karimi 5
1 - Ph.D Student of Environment, Research Institute for Grapes and Raisin (RIGR), Malayer University
2 - Assis. Prof. College of Forest Science and Engieering, Department of Natural Resources and Earth Sciences, Shahrekord University
3 - Prof. College of Water Engineering, Department of Agriculture, Bu-Ali Sina University, Hamedan
4 - Assis. Prof. College of Environment, Department of Natural Resources and Environments, Malayer University
5 - Assis. Prof. College of Green Space Design, Department of Agriculture, Malayer University
Keywords: Artificial Neural Network (ANN), Support vector machine (SVM), Plant species discrimination, Field spectroscopy, Spectral indices,
Abstract :
Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees. After the initial correction, 30 of the most important spectral indices were extracted. Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1,200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species before analyzing the hyperspectral images due to their large data volume, high cost and huge data analysis.
بخشوده، م. و ح. شفیعی. 1385. بررسی اثرات حمایتی سیاست خرید تضمینی روی سطح زیر کشت و عملکرد پنبه، سیب‌زمینی و پیاز در استان فارس. نشریه علوم آب‌وخاک، 10(3): 164-257.
پیشنماز احمدی، م.، م. ح. رضائی مقدم و ب. فیضی زاده. 1396. بررسی شاخص‌ها و تهیۀ نقشه شوری خاک با استفاده از دادههای سنجش‌ازدور (مطالعۀ موردی: دلتای آجی‌چای). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 8(1): 85-96.
درویش‌صفت، ع. ا.، م. عباسی و م. مروی مهاجر. 1388. امکان تهیه نقشه تیپ راش به کمک دادههای سنجنده ETM+. مجله جنگل ایران، 1(2): 105-113.
رحیم‌زادگان، م. و م. پورغلام. 1395. تعیین سطح زیر کشت گیاه زعفران با استفاده از تصاویر لندست (مطالعۀ موردی: شهرستان تربت حیدریه). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(4): 97-115.
سالنامه آماری استان چهارمحال و بختیاری. 1392. فصل 4 : کشاورزی، جنگلداری و شیلات. 42 صفحه.
عباسی، م.، ع. درویش‌صفت، م. شپمن، ه. سبحانی، ا. شیروانی و ش. شبستانی. 1390. تفاوت انعکاس طیفی برگ گونه‌های درختی توسکا، بلوط و انجیلی بر اساس غلظت نیتروژن و استفاده از رگرسیون چند متغیره. جنگل و فرآورده‌های چوب، 64(4): 399-417.
میرزائی، م.، ع. ریاحی بختیاری، ع. سلمان ماهینی و م. غلامعلی فرد. 1395. مدلسازی ارتباط کیفیت آب‌های سطحی و سنجه‌های سیمای سرزمین با استفاده از سیستم استنتاج عصبی – فازی (مطالعۀ موردی: استان مازندران). مجله آب و فاضلاب، 27(1): 82-91.
نجفی، ا. و م. ح. مختاری. 1394. مقایسه روش‌های طبقهبندی ماشینبردار پشتیبان و شبکه عصبی مصنوعی در استخراج کاربری‌های اراضی از تصاویر ماهواره‌ای لندست TM. مجله علوم و فنون کشاورزی و منابع طبیعی. 19(7): 35-44.
Adam E, Mutanga O. 2009. Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 612-620.
Adam E, Mutanga O, Rugege D. 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management. 18(3): 281-296.
Aneece I, Epstein H. 2017. Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia. International Journal of Remote Sensing, 38(1): 100-122.
ASD, Analytical Spectral Devices, Inc. 2005. Handheld Spectroradiometer: User’s Guide, Version 4.05. Boulder, USA.
Atherton D, Choudhary R, Watson D. 2017. Hyperspectral Remote Sensing for Advanced Detection of Early Blight (Alternaria solani) Disease in Potato (Solanum tuberosum) Plants Prior to Visual Disease Symptoms. In: 2017 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, 10 pp.
Barnes JD, Balaguer L, Manrique E, Elvira S, Davison AW. 1992. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environmental and Experimental Botany, 32(2): 85-100.
Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S. 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2): 216-225.
Belluco E, Camuffo M, Ferrari S, Modenese L, Silvestri S, Marani A, Marani M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1): 54–67.
Bratsch SN, Epstein HE, Buchhorn M, Walker DA. 2016. Differentiating among four Arctic tundra plant communities at Ivotuk, Alaska using field spectroscopy, Remote Sensing, 8(1): 45-51.
Carter GA. 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote sensing, 15(3): 697-703.
Cho MA, Sobhan I, Skidmore AK, De Leeuw J. 2008. Discriminating species using hyperspectral indices at leaf and canopy scales. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. 369-376.
Clark ML, Roberts DA. 2012. Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sensing, 4(6): 1820-1855.
Clevers JG, Kooistra L, Schaepman ME. 2010. Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 12(2): 119-125.
Datt B. 1999. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14): 2741-2759.
Galvão LS, Formaggio AR, Tisot DA. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote sensing of Environment, 94(4): 523-534.
Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Valentini R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1): 28-41.
Gao BC. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3): 257-266.
Gitelson AA, Merzlyak MN. 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12): 2691-2697.
Gong P, Pu R, Heald RC. 2002. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 23(9): 1827-1850.
Kalacska M, Bohlman S, Sanchez-Azofeifa GA, Castro-Esau K, Caelli T. 2007. Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment, 109(4): 406-415.
Kent M. 2011. Vegetation description and data analysis: a practical approach. John Wiley & Sons. 432 pp.
Koch B. 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6): 581-590.
Lehmann JRK, Große-Stoltenberg A, Römer M, Oldeland, J. 2015. Field spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content. Remote Sensing, 7(2): 1225-1241.
Lichtenthaler HK, Lang M, Sowinska M, Heisel F, Miehe JA. 1996. Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of plant physiology, 148(5): 599-612.
Löw F, Fliemann E, Abdullaev I, Conrad C, Lamers JP. 2015. Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing. Applied Geography, 62(1): 377-390.
Merton R. 1998. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. In Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 12-16 January, 12-16.
Mureriwa N, Adam E, Sahu A, Tesfamichael S. 2016. Examining the Spectral Separability of Prosopis glandulosa from co-existent species using Field spectral measurement and guided regularized random forest. Remote Sensing, 8(2):130-144.
Murphy RJ, Monteiro ST, Schneider S. 2012. Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3066-3080.
Nagler PL, Inoue Y, Glenn EP, Russ AL, Daughtry CST. 2003. Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2): 310-325.
Najah AA, El-Shafie OA, Karim O, Jaafar A, El-Shafie A. 2011. An application of different artificial intelligences techniques for water quality prediction. International Journal of the Physical Sciences, 6(22): 5298-5308.
Peñuelas J, Filella I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4): 151-156.
Peñuelas J, Baret F, Filella I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2): 221-230.
Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB. 1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote sensing of Environment, 48(2): 135-146.
Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13): 2869-2875.
Prospere K, McLaren K, Wilson B. 2014. Plant species discrimination in a tropical wetland using in situ hyperspectral data. Remote sensing, 6(9): 8494-8523.
Pu R. 2009. Broadleaf species recognition with in situ hyperspectral data. International Journal of Remote Sensing, 30(11): 2759-2779.
Pu R, Ge S, Kelly NM, Gong P. 2003. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24(9): 1799-1810.
Raynolds MK, Walker DA, Epstein HE, Pinzon JE, Tucker CJ. 2012. A new estimate of tundra-biome phytomass from trans arctic field data and AVHRR NDVI. Remote Sensing Letters, 3(5): 403-411.
Rivard B, Sanchez-Azofeifa GA, Foley S, Calvo-Alvarado JC. 2008. Species classification of tropical tree leaf reflectance and dependence on selection of spectral bands. Hyperspectral Remote Sensing of Tropical and Sub Tropical Forests, 141-159.
Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 81(2): 337-354.
Sims DA, Gamon JA. 2003. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84(4): 526-537.
Song XP, Potapov P, Adusei B, King L, Khan A, Krylov A, Hansen M. 2016. National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey. In AGU Fall Meeting Abstracts. 1-5.
Stitson M, Weston J, Gammerman A, Vovk V, Vapnik VJUoL. 1996. Theory of support vector machines. 117 (827):188-191.
Thenkabail PS, Lyon JG. 2016. Hyperspectral remote sensing of vegetation. CRC Press, 782 pp.
Thomas J, Gausman HW. 1977. Leaf reflectance vs. leaf chlorophyll and carotenoid cncentrations for eight crops. Agronomy Journal, 69 (5): 799-802.
Vaiphasa C, Ongsomwang S, Vaiphasa T, Skidmore AK. 2005. Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine, Coastal and Shelf Science, 65(1): 371-379.
Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8): 1563-1575.
Wu W, Walczak B, Massart DL, Heuerding S, Erni F, Last IR, Prebble KA. 1996. Artificial neural networks in classification of NIR spectral data: Design of the training set. Chemometrics and Intelligent Laboratory Systems, 33(1): 35-46.
Yang YH, Yiao Y, Segal MR. 2005. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21(7): 1084-1093.
Zhang M, Qin Z, Liu X, Ustin SL. 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4(4): 295-310.
بخشوده، م. و ح. شفیعی. 1385. بررسی اثرات حمایتی سیاست خرید تضمینی روی سطح زیر کشت و عملکرد پنبه، سیب‌زمینی و پیاز در استان فارس. نشریه علوم آب‌وخاک، 10(3): 164-257.
پیشنماز احمدی، م.، م. ح. رضائی مقدم و ب. فیضی زاده. 1396. بررسی شاخص‌ها و تهیۀ نقشه شوری خاک با استفاده از دادههای سنجش‌ازدور (مطالعۀ موردی: دلتای آجی‌چای). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 8(1): 85-96.
درویش‌صفت، ع. ا.، م. عباسی و م. مروی مهاجر. 1388. امکان تهیه نقشه تیپ راش به کمک دادههای سنجنده ETM+. مجله جنگل ایران، 1(2): 105-113.
رحیم‌زادگان، م. و م. پورغلام. 1395. تعیین سطح زیر کشت گیاه زعفران با استفاده از تصاویر لندست (مطالعۀ موردی: شهرستان تربت حیدریه). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(4): 97-115.
سالنامه آماری استان چهارمحال و بختیاری.1392. فصل 4 : کشاورزی، جنگلداری و شیلات. 42 صفحه.
عباسی، م.، ع. درویش‌صفت، م. شپمن، ه. سبحانی، ا. شیروانی و ش. شبستانی. 1390. تفاوت انعکاس طیفی برگ گونه‌های درختی توسکا، بلوط و انجیلی بر اساس غلظت نیتروژن و استفاده از رگرسیون چند متغیره. جنگل و فرآورده‌های چوب، 64(4): 399-417.
میرزائی، م.، ع. ریاحی بختیاری، ع. سلمانماهینی و م. غلامعلیفرد. 1395.مدلسازی ارتباط کیفیت آب‌های سطحی و سنجه‌های سیمای سرزمین با استفاده از سیستم استنتاج عصبی – فازی (مطالعۀ موردی: استان مازندران). مجله آب و فاضلاب، 27(1): 82-91.
نجفی، ا. و م. ح. مختاری. 1394. مقایسه روش‌های طبقهبندی ماشینبردار پشتیبان و شبکه عصبی مصنوعی در استخراج کاربری‌های اراضی از تصاویر ماهواره‌ای لندست TM. مجله علوم و فنون کشاورزی و منابع طبیعی. 19(7): 35-44.
Adam E, Mutanga O. 2009. Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 612-620.
Adam E, Mutanga O, Rugege D. 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management. 18(3): 281-296.
Aneece I, Epstein H. 2017. Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia. International Journal of Remote Sensing, 38(1): 100-122.
ASD, Analytical Spectral Devices, Inc. 2005. Handheld Spectroradiometer: User’s Guide, Version 4.05. Boulder, USA.
Atherton D, Choudhary R, Watson D. 2017. Hyperspectral Remote Sensing for Advanced Detection of Early Blight (Alternaria solani) Disease in Potato (Solanum tuberosum) Plants Prior to Visual Disease Symptoms. In: 2017 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, 10 pp.
Barnes JD, Balaguer L, Manrique E, Elvira S, Davison AW. 1992. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environmental and Experimental Botany, 32(2): 85-100.
Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S. 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2): 216-225.
Belluco E, Camuffo M, Ferrari S, Modenese L, Silvestri S, Marani A, Marani M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1): 54–67.
Bratsch SN, Epstein HE, Buchhorn M, Walker DA. 2016. Differentiating among four Arctic tundra plant communities at Ivotuk, Alaska using field spectroscopy, Remote Sensing, 8(1): 45-51.
Carter GA. 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote sensing, 15(3): 697-703.
Cho MA, Sobhan I, Skidmore AK, De Leeuw J. 2008. Discriminating species using hyperspectral indices at leaf and canopy scales. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. 369-376.
Clark ML, Roberts DA. 2012. Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sensing, 4(6): 1820-1855.
Clevers JG, Kooistra L, Schaepman ME. 2010. Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 12(2): 119-125.
Datt B. 1999. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14): 2741-2759.
Galvão LS, Formaggio AR, Tisot DA. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote sensing of Environment, 94(4): 523-534.
Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Valentini R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1): 28-41.
Gao BC. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3): 257-266.
Gitelson AA, Merzlyak MN. 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12): 2691-2697.
Gong P, Pu R, Heald RC. 2002. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 23(9): 1827-1850.
Kalacska M, Bohlman S, Sanchez-Azofeifa GA, Castro-Esau K, Caelli T. 2007. Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment, 109(4): 406-415.
Kent M. 2011. Vegetation description and data analysis: a practical approach. John Wiley & Sons. 432 pp.
Koch B. 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6): 581-590.
Lehmann JRK, Große-Stoltenberg A, Römer M, Oldeland, J. 2015. Field spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content. Remote Sensing, 7(2): 1225-1241.
Lichtenthaler HK, Lang M, Sowinska M, Heisel F, Miehe JA. 1996. Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of plant physiology, 148(5): 599-612.
Löw F, Fliemann E, Abdullaev I, Conrad C, Lamers JP. 2015. Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing. Applied Geography, 62(1): 377-390.
Merton R. 1998. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. In Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 12-16 January, 12-16.
Mureriwa N, Adam E, Sahu A, Tesfamichael S. 2016. Examining the Spectral Separability of Prosopis glandulosa from co-existent species using Field spectral measurement and guided regularized random forest. Remote Sensing, 8(2):130-144.
Murphy RJ, Monteiro ST, Schneider S. 2012. Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3066-3080.
Nagler PL, Inoue Y, Glenn EP, Russ AL, Daughtry CST. 2003. Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2): 310-325.
Najah AA, El-Shafie OA, Karim O, Jaafar A, El-Shafie A. 2011. An application of different artificial intelligences techniques for water quality prediction. International Journal of the Physical Sciences, 6(22): 5298-5308.
Peñuelas J, Filella I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4): 151-156.
Peñuelas J, Baret F, Filella I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2): 221-230.
Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB. 1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote sensing of Environment, 48(2): 135-146.
Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13): 2869-2875.
Prospere K, McLaren K, Wilson B. 2014. Plant species discrimination in a tropical wetland using in situ hyperspectral data. Remote sensing, 6(9): 8494-8523.
Pu R. 2009. Broadleaf species recognition with in situ hyperspectral data. International Journal of Remote Sensing, 30(11): 2759-2779.
Pu R, Ge S, Kelly NM, Gong P. 2003. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24(9): 1799-1810.
Raynolds MK, Walker DA, Epstein HE, Pinzon JE, Tucker CJ. 2012. A new estimate of tundra-biome phytomass from trans arctic field data and AVHRR NDVI. Remote Sensing Letters, 3(5): 403-411.
Rivard B, Sanchez-Azofeifa GA, Foley S, Calvo-Alvarado JC. 2008. Species classification of tropical tree leaf reflectance and dependence on selection of spectral bands. Hyperspectral Remote Sensing of Tropical and Sub Tropical Forests, 141-159.
Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 81(2): 337-354.
Sims DA, Gamon JA. 2003. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84(4): 526-537.
Song XP, Potapov P, Adusei B, King L, Khan A, Krylov A, Hansen M. 2016. National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey. In AGU Fall Meeting Abstracts. 1-5.
Stitson M, Weston J, Gammerman A, Vovk V, Vapnik VJUoL. 1996. Theory of support vector machines. 117 (827):188-191.
Thenkabail PS, Lyon JG. 2016. Hyperspectral remote sensing of vegetation. CRC Press, 782 pp.
Thomas J, Gausman HW. 1977. Leaf reflectance vs. leaf chlorophyll and carotenoid cncentrations for eight crops. Agronomy Journal, 69 (5): 799-802.
Vaiphasa C, Ongsomwang S, Vaiphasa T, Skidmore AK. 2005. Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine, Coastal and Shelf Science, 65(1): 371-379.
Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8): 1563-1575.
Wu W, Walczak B, Massart DL, Heuerding S, Erni F, Last IR, Prebble KA. 1996. Artificial neural networks in classification of NIR spectral data: Design of the training set. Chemometrics and Intelligent Laboratory Systems, 33(1): 35-46.
Yang YH, Yiao Y, Segal MR. 2005. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21(7): 1084-1093.
Zhang M, Qin Z, Liu X, Ustin SL. 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4(4): 295-310.
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