Identification of Damage to Coniferous Stands Based on Comprehensive Analysis of the Results of Remote Sensing of the Earth and Ground Surveys
DOI:
https://doi.org/10.37482/0536-1036-2024-2-11-28Keywords:
ICP-Forests, state classes, remote sensing methods, Sentinel-2B, automatic image classification, support vector machine (SVM), vegetation index, normalized difference moisture index (NDMI), normalized burn ratio (NBR), thematic map, the Leningrad RegionAbstract
Damage to forest stands and their death as a result of outbreaks of destructive insects and diseases occur over significant areas and represent an important forestry problem. To solve this problem, it is advisable to use materials from remote sensing of the Earth, since damaged and dead forests can occupy significant areas, from remote еach other. The article presents a methodology for assessing the state of forest stands and the impact of forest pests and diseases on them based on the joint processing of data from a regular grid of sample plots and materials from remote sensing of the Earth via geoinformational technologies, variance and regression analyses. The sample plots have been laid according to the ICPForests methodology and have characterized the general background of the state of the forests in the study area of the North-Western and central parts of the Leningrad Region, where the foci of forest pests and diseases reproduction have been identified at the same time. The applicability of 2 vegetation indices – normalized difference (NDVI) and short–wave (SWVI), as well as the normalized difference moisture index (NDMI), the normalized burn ratio (NBR) and the spectral characteristics of the corresponding channels of the Sentinel-2B image to identify the foci of forest damage by pests and diseases has been assessed. Statistically significant relationships between the state of the stands and the values of the listed indices have been established. It has been shown that the foci of pests and diseases reproduction are reliably detected using the materials from remote sensing of the Earth against the general background of the state of the forests represented by the regular grid of sample plots. A thematic map of the stands has been developed dividing them into 2 groups of state classes: healthy and weakened stands; suppressed stands and deadwood. Its accuracy was assessed via the support vector machine (SVM) on the basis of the combination of ground and remote sensing data. To assess the adequacy of the automatic classification, the error matrix and the calculation of the Cohen’s Kappa coefficient have been used. The coefficient has turned out equal to 0.878, which indicates a high quality of the classification. It has been shown that the developed thematic map is applicable for detecting potential foci of forest pests and diseases reproduction in the study area.
Downloads
References
Ахматович Н.А., Селиховкин А.В., Магдеев Н.Г. Управление рисками в Республике Татарстан: вредители и болезни основных лесообразующих пород // Изв. вузов. Лесн. журн. 2015. № 1. C. 21–34. Akhmatovich N.A., Selikhovkin A.V., Magdeev N.G. Risk Management in the Republic of Tatarstan: Pests and Diseases of the Main Forest Forming Plants. Lesnoy Zhurnal = Russian Forestry Journal, 2015, no. 1, pp. 21–34. (In Russ.). https://doi.org/10.17238/issn0536-1036.2015.1.21
Аэрокосмические методы в охране природы и в лесном хозяйстве / под ред. В.И. Сухих, С.Г. Синицына. М.: Лесн. пром-сть, 1979. 287 с. Aerospace Methods in Nature Conservation and Forestry. Ed. by V.I. Sukhikh, S.G. Sinitsyna. Moscow, Lesnaya promyshlennost’ Publ., 1979. 287 p. (In Russ.).
Барталев С.А., Егоров В.А., Крылов А.М., Стыценко Ф.В., Ховратович Т.С. Исследование возможностей оценки состояния поврежденных пожарами лесов по данным многоспектральных спутниковых измерений // Соврем. проблемы дистанц. зондирования Земли из космоса. 2010. Т. 7, № 3. С. 215–225. Bartalev S.A., Egorov V.A., Krylov A.M., Stytsenko F.V., Khovratovich T.S. The Evaluation of Possibilities to Assess Forest Burnt Severity Using Multi-Spectral Satellite Data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2010, vol. 7, no. 3, pp. 215–225. (In Russ.).
Барталев С.А., Стыценко Ф.В., Егоров В.А., Лупян Е.А. Спутниковая оценка гибели лесов России от пожаров // Лесоведение. 2015. № 2. С. 83–94. Bartalev S.A., Stytsenko F.V., Egorov V.A., Loupian E.A. Satellite-Based Asessment of Russian Forest Fire Mortality. Lesovedenie = Russian Journal of Forest Science, 2015, no. 2, pp. 83–94. (In Russ.).
Барталев С.А., Стыценко Ф.В., Хвостиков С.А., Лупян Е.А. Методология мониторинга и прогнозирования пирогенной гибели лесов на основе данных спутниковых наблюдений // Соврем. проблемы дистанц. зондирования Земли из космоса. 2017. Т. 14, № 6. С. 176–193. Bartalev S.A., Stytsenko F.V., Khvostikov S.A., Loupian E.A. Methodology of Post-Fire Tree Mortality Monitoring and Prediction Using Remote Sensing Data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2017, vol. 14, no. 6, pp. 176–193. (In Russ.). https://doi.org/10.21046/2070-7401-2017-14-6-176-193
Воробьев О.Н., Курбанов Э.А., Демишева Е.Н., Меньшиков С.А., Али М.С., Смирнова Л.Н., Тарасова Л.В. Дистанционный мониторинг устойчивости лесных экосистем / под общ. ред. проф. Э.А. Курбанова. Йошкар-Ола: ПГТУ, 2019. 166 с. Vorobiev O.N., Kurbanov E.A., Demisheva E.N., Menshikov S.A., Ali M.C., Smirnova L.N., Tarasova L.V. Remote Monitoring of Forest Ecosystems Sustainability. Ed. by prof. E.A. Kurbanov. Yoshkar-Ola, Volga State University of Technology, 2019. 166 p. (In Russ.).
Григорьева О.В. Наблюдение деградации лесов по данным гиперспектрального аэро- и космического зондирования // Исследование Земли из космоса. 2014. № 1. С. 43–48. Grigoryeva O.V. Observation of Forest Degradation Using Hyperspectral Data Aerial and Satellite Sensing. Earth Observation and Remote Sensing, 2014, no. 1, pp. 43–48. (In Russ.). https://doi.org/10.7868/S020596141306002X
Карпов А., Васке Б. Метод отнесения земель к землям, занятым лесной растительностью, по аэрокосмическим снимкам Landsat // Изв. вузов. Лесн. журн. 2020. № 3. С. 83–92. Karpov A., Waske B. Method for Transferring Non-Forest Cover to Forest Cover Land Using Landsat Imageries. Lesnoy Zhurnal = Russian Forestry Journal, 2020, no. 3, pp. 83–92. (In Russ.). https://doi.org/10.37482/0536-1036-2020-3-83-92
Князева С.В., Эйдлина С.П. Картографическая оценка динамики показателей состояния древесных растений северо-западных регионов России // Вопр. лесн. науки. 2018. Т. 1(1). С. 1–33. Knyazeva S.V., Eydlina S.P. Cartographic Estimation of Tree Parameter Dynamics in Russian Northwest Regions. Voprosy lesnoy nauki = Forest Science Issues, 2018, vol. 1(1), pp. 1–33. (In Russ.). https://doi.org/10.31509/2658-607X-2018-1-1-1-33
Ковалев А.В. Анализ устойчивости лесных насаждений к повреждениям сибирским шелкопрядом по данным дистанционного зондирования // Сиб. лесн. журн. 2021. № 5. С. 71–78. Kovalev A.V. Analysis of Forest Stands Resistance to Siberian Silkmoth Attack According to Remote Sensing Data. Sibirskiy lesnoy zhurnal = Siberian Journal of Forest Science, 2021, no. 5, pp. 71–78. (In Russ.). https://doi.org/10.15372/SJFS20210508
Крылов А.М., Соболев А.А., Владимирова Н.А. Выявление очагов короеда-типографа в Московской области с использованием снимков Landsat // Вестн. МГУЛ – Лесн. вестн. 2011. № 4. С. 54–60. Krylov A.M., Sobolev A.A., Vladimirova N.А. Revealing of Centers Ips typographus in Moscow Region with Use of Pictures Landsat. Lesnoy vestnik = Forestry Bulletin, 2011, no. 4, pp. 54–60. (In Russ.).
Малахова Е.Г., Крылов А.М. Усыхание ельников в Клинском лесничестве Московской области // Изв. СамНЦ РАН. 2012. Т. 14, № 1(8). С. 1975–1978. Malakhova E.G., Krylov A.M. The Fir Groves Drying in Klinsky Forestry of Moscow Oblast. Izvestia of Samara Scientific Center of the Russian Academy of Sciences, 2012, vol. 14, no. 1(8), pp. 1975–1978. (In Russ.).
Методы мониторинга вредителей и болезней леса / под общ. ред. В.К. Тузова. М.: ВНИИЛМ, 2004. 200 с. Methods for Monitoring Forest Pests and Diseases. Ed. by V.K. Tuzov. Moscow, VNIILM Publ., 2004. 200 p. (In Russ.).
Силкина О.В., Винокурова Р.И. Сезонная динамика содержания хлорофиллов и микроэлементов в формирующейся хвое Abies sibirica и Picea abies // Физиология растений. 2009. Т. 56, № 6. С. 864–870. Silkina O.V., Vinokurova R.I. Seasonal Dynamics of Chlorophyll and Microelement Content in Developing Conifer Needles of Abies sibirica and Picea abies. Fisiologiya Rasteniy = Russian Journal of Plant Physiology, 2009, vol. 56, no. 6, pp. 864–870. (In Russ.).
Стыценко Ф.В., Барталев С.А., Букась А.В., Ершов Д.В., Сайгин И.А. Возможности пролонгированной оценки постпожарного состояния хвойных вечнозеленых лесов по данным многоспектральных спутниковых измерений // Соврем. проблемы дистанц. зондирования Земли из космоса. 2019. Т. 16, № 5. С. 217–227. Stytsenko F.V., Bartalev S.A., Bukas A.V., Ershov D.V., Saigin I.A. The Possibilities of Prolonged Burnt Severity Assessment of Evergreen Coniferous Forest Using Multi-Spectral Satellite Data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2019, vol. 16, no. 5, pp. 217–227. (In Russ.). https://doi.org/10.21046/2070-7401-2019-16-5-217-227
Сухих В.И. Аэрокосмические методы в лесном хозяйстве и ландшафтном строительстве. Йошкар-Ола: МарГТУ, 2005. 392 с. Sukhikh V.I. Aerospace Methods in Forestry and Landscape Construction. Yoshkar-Ola, MarSTU Publ., 2005. 392 p. (In Russ.).
Токарева О.С. Обработка и интерпретация данных дистанционного зондирования Земли. Томск: ТПУ, 2010. 148 с. Tokareva O.S. Processing and Interpretation of the Earth Remote Sensing Data. Tomsk, TPU Publ., 2010. 148 p. (In Russ.).
Федотова Е.В., Заречнева А.И. Пространственно-временная динамика вспышки массового размножения сибирского шелкопряда в темнохвойных древостоях Горного Алтая // Журн. СФУ. Сер.: Техника и технологии. 2017. Т. 10, № 6. С. 747–757. Fedotova E.V., Zarechneva A.I. Spatial-Temporal Dinamics of Siberian Silkmoth Outbreak in Dark Needle Coniferous Forest in Altay Mountains. Zhurnal Sibirskogo federal’nogo universiteta. Tekhnika i tekhnologii = Journal of Siberian Federal University. Engineering & Technologies, 2017, vol. 10, no. 6, pp. 747–757. (In Russ.). https://doi.org/10.17516/1999-494X-2017-10-6-747-757
Хумала А.Э., Полевой А.В., Шалаев В.С., Галкин Ю.С., Щербаков А.Н., Налдеев Д.Ф., Никула А., Ройнинен Х. Оценка возможности применения аэрокосмических методов для мониторинга лесного покрова в Национальном парке «Водлозерский» // Водлозерские чтения: естественнонаучные и гуманитарные основы природоохранной, научной и просветительской деятельности на охраняемых природных территориях Русского Севера: материалы науч.-практ. конф., посвящ. 15-летию Нац. парка «Водлозерский», Петрозаводск, 27–28 апр. 2006 г. Петрозаводск: КарНЦ, 2006. С. 106–112. Khumala A.E., Polevoy A.V., Shalaev V.S., Galkin Yu.S., Shcherbakov A.N., Naldeev D.F., Nikula A., Rojninen H. Assessment of the Possibility of Using Aerospace Methods for Monitoring Forest Cover in the «Vodlozersky» National Park. Vodlozersky Readings: Natural Science and Humanitarian Foundoctions of Environmental, Scientific and Educational Activities in Protected Natural Areas of the Russian North: Materials of the Scientific and Practical Conference Dedicated to the 15th Anniversary of the “Vodlozersky” National Park (Petrozavodsk, April 27–28, 2006). Petrozavodsk, Karelian Research Centre Publ., 2006, pp. 106–112. (In Russ.).
Черепанов А.С., Дружинина Е.Г. Спектральные свойства растительности и вегетационные индексы // Геоматика. 2009. № 3. С. 28–32. Cherepanov A., Druzhinina E. Spectral Characteristics of Vegetation and Vegetation Indexes. Geomatica = Geomatics, 2009, no. 3, pp. 28–32. (In Russ.).
Шаталов А.В., Жирин В.М., Сухих В.И., Эйдлина С.П., Шалаев В.С. К оценке повреждения лесов Национального парка «Водлозерский» по космическим изображениям // Водлозерские чтения: естественнонаучные и гуманитарные основы природоохранной, научной и просветительской деятельности на охраняемых природных территориях Русского Севера: материалы науч.-практ. конф., посвящ. 15-летию Нац. парка «Водлозерский», Петрозаводск, 27–28 апр. 2006 г. Петрозаводск: КарНЦ, 2006. С. 102–106. Shatalov A.V., Zhirin V.M., Sukhikh V.I., Eydlina S.P., Shalaev V.S. To the Assessment of Damage to the Forests of the «Vodlozersky» National Park Based on Satellite Images. Natural Science and Humanitarian Foundoctions of Environmental, Scientific and Educational Activities in Protected Natural Areas of the Russian North. Materials of the Scientific and Practical Conference Dedicated to the 15th Anniversary of the “Vodlozersky” National Park (Petrozavodsk, April 27–28, 2006). Petrozavodsk, Karelian Research Centre Publ., 2006, pp. 102–106. (In Russ.).
Шелухо В.П., Шошин В.И., Клюев В.С. Динамика санитарного состояния ельников в период кульминации размножения типографа и эффективность лесозащитных мероприятий // Изв. вузов. Лесн. журн. 2014. № 2. С. 30–39. Shelukho V.P., Shoshin V.I., Klyuev V.S. Sanitary State Dynamics of Spruce Forests Under Culminating Ips typographus Reproduction and Efficiency of Forest-Protection Measures. Lesnoy Zhurnal = Russian Forestry Journal, 2014, no. 2, pp. 30–39. (In Russ.).
Alekseev A., Chernikhovskii D. Assessment of the Health Status of Tree Stands Based on Sentinel - 2B Remote Sensing Materials and the Short-Wave Vegetation Index SWVI. IOP Conference Series: Earth and Environmental Science, 2021, vol. 876, art. no. 012003. https://doi.org/10.1088/1755-1315/876/1/012003
Alekseev A., Chernikhovskii D., Vetrov L., Gurjanov M., Nikiforchin I. Determination of the State of Forests Based on a Regular Grid of Ground-Based Sample Plots and Sentinel-2B Satellite Imagery Using the k-NN («Nearest Neighbour») Method. IOP Conference Series: Earth and Environmental Science, 2021, vol. 876, art. no. 012002. https://doi.org/10.1088/1755-1315/876/1/012002
Congalton R.G., Green K. Assessing the Accuracy of Remotely Sensed Data. Principles and Practices. 3rd ed. Boca Raton, CRC Press, 2019. 346 p. https://doi.org/10.1201/9780429052729
Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Böhner J. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 2015, vol. 8, iss. 7, pp. 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Fan R.-E., Chen P.-H., Lin C.-J. Working Set Selection Using Second Order Information for Training Support Vector Machines. Journal of Machine Learning Research, 2005, vol. 6, pp. 1889–1918.
Fisher R., Hobgen S., Mandaya I., Kaho N., Zulkarnain N. Satellite Image Analysis and Terrain Modelling. A Practical Manual for Natural Resource Management, Disaster Risk and Development Planning Using Free Geospatial Data and Software. Version 2. SAGA GIS 4. Charles Darwin University, 2017. 150 p.
Franklin S.E. Remote Sensing for Sustainable Forest Management. Boca Raton, CRC Press, 2001. 424 p. https://doi.org/10.1201/9781420032857
Kharuk V.I., Ranson K.J., Kozuhovskaya A.G., Kondakov Y.P., Pestunov I.A. NOAA/AVHRR Satellite Detection of Siberian Silkmoth Outbreaks in Eastern Siberia. International Journal of Remote Sensing, 2004, vol. 25, iss. 24, pp. 5543–5556. https://doi.org/10.1080/01431160410001719858
Hsu C.-W., Chang C.-C., Lin C.-J. A Practical Guide to Support Vector Classification, 2016. 16 p.
Lillesand T.M., Kiefer R.W., Chipman J.W. Remote Sensing and Image Interpretation. 7th ed. New York, Wiley Inc., 2015. 736 p.
Lukeš P. Monitoring of Bark Beetle Forest Damages. Big Data in Bioeconomy. Springer Publ., 2021, chapt. 26, pp. 351–361. https://doi.org/10.1007/978-3-030-71069-9_26
Nelson R.F. Detecting Forest Canopy Change Due to Insect Activity Using Landsat MSS. Photogrammetric Engineering and Remote Sensing, 1983, vol. 49, no. 9, pp. 1303–1314.
Olsson P.-O., Kantola T., Lyytikäinen-Saarenmaa P., Jönsson A.M., Eklundh L. Development of a Method for Monitoring of Insect Induced Forest Defoliation – Limitation of MODIS Data in Fennoscandian Forest Landscapes. Silva Fennica, 2016, vol. 50, no. 2, art. no. 1495. https://doi.org/10.14214/sf.1495
Olsson P.-O., Lindström J., Eklundh L. Near Real–Time Monitoring of Insect Induced Defoliation in Subalpine Birch Forests with MODIS derived NDVI. Remote Sensing of Environment, 2016, vol. 181, pp. 42–53. https://doi.org/10.1016/j.rse.2016.03.040
QGIS and Applications in Agriculture and Forest. Vol. 2. Ed. by N. Baghdadi, C. Mallet, M. Zribi. Wiley Inc., 2018. 368 p.
Zhirin V.M., Knyazeva S.V., Eydlina S.P. Dynamics of Spectral Brightness of the Species/Age Structure for Groups of Forest Types on Landsat Satellite Images. Contemporary Problems of Ecology, 2014, vol. 7, pp. 788–796. https://doi.org/10.1134/S1995425514070142
Zhirin V.M., Knyazeva S.V., Eydlina S.P. Long-Term Dynamics of Vegetation Indices in Dark Coniferous Forest after Siberian Moth Disturbance. Contemporary Problems of Ecology, 2016, vol. 9, pp. 834–843. https://doi.org/10.1134/S1995425516070118
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 А.С. Алексеев, Д.М. Черниховский (Автор)
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.