APPLICATION OF THE THRESHOLD SEGMENTATION METHOD FOR ASSESSING FOREST CHARACTERISTICS BASED ON HIGH-DETAILED RESURS-P1 SATELLITE DATA

S. V. Knyazeva*, A. D. Nikitina, E. I. Belova

 

Isaev Centre for Forest Ecology and Productivity of the Russian Academy of Sciences,

Profsoyuznaya st., 84/32, bldg. 14, Moscow, 117997 Russia

 

*E-mail: knsvetl@gmail.com

Received: 08.10.2025

Revised: 17.11.2025

Accepted: 28.11.2025

This article presents the results of a study examining the potential of threshold segmentation of intercrown areas of forest canopy images using domestic ultra-high-resolution satellite images obtained from the Resurs-P1 (Geoton-L) satellite to identify the relationship between segmentation parameters and biometric characteristics of pine stands, using the forests of the Curonian Spit National Park as an example. The proposed method is based on identifying shaded segments of the intercrown space within forest stand boundaries, taking into account a specified brightness range, and then merging adjacent pixels based on spectral proximity at a new specified brightness threshold. For each specified threshold, the areas and average brightness values ​​of shadow segments within the stand boundaries, standard deviations, and median values ​​are determined. Based on these values, a threshold canopy closure is calculated for each stand, taking into account only shaded intercrown spaces. Statistical characteristics of average brightness and canopy closure threshold serve as variables for regression modeling of biometric characteristics (height, diameter, and stand age) of pine forests.

The regression analysis was conducted using an ensemble method with Random Forest (RF) decision tree construction. The R² coefficient of determination for pine forest characteristics ranges from 0.29 to 0.37. The results of the validation model for the test set are virtually identical to those for the training set, demonstrating the robustness of the RF model. Regression modeling of pine stand characteristics using the RF algorithm (using pure pine stands in the Curonian Spit National Park as an example), using predictors derived from threshold segmentation of forest canopy images on Geoton-L panchromatic images, yields stable results with a root-mean-square error of approximately 4 m for average height, 6 cm for diameter, and 20 years for age. Threshold segmentation of tree canopy images is useful for preliminary assessment of stand characteristics in cases where radiometric correction of spectral data is insufficient for calculating standard textural characteristics.

Keywords: pine forests biometric characteristics, ultra-high spatial resolution satellite images, threshold image segmentation, texture features, regression modeling

REFERENCES

Aleksanin A. I., Kim V., Obnaruzhenie rubok po tenyam (Detection of logging sites) VI Mezhdunarodnaya nauchnaya konferenciya «Regional’nye problemy distancionnogo zondirovaniya Zemli» (6th International Scientific Conference “Regional Problems of Remote Sensing of the Earth”), Sibirskij federal’nyj universitet, Institut kosmicheskih i informacionnyh tekhnologij, 2019, pp. 66–68.

Aleksanina M. G., Khramtsova A. V., Obnaruzhenie melkomasshtabnoj izmenchivosti lesnogo pologa na sputnikovyh panhromaticheskih izobrazheniyah na osnove matricy smezhnosti perepadov yarkosti (Detection of small-scale forest canopy variability in satellite panchromatic images based on brightness difference adjacency matrix), Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No 4, pp. 47–59.

Beguet B., Guyon D., Boukir S., Chehata N., Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 2014, Vol. 96. pp. 164–178.

Denisova A. Yu., Egorova A. A., Sergeev V. V., Kavelenova L. M., Vyrabotka trebovanij k mul’tispektral’nym dannym distancionnogo zondirovaniya Zemli v zadache ekspertizy zarastaniya pahotnyh zemel’ drevesno-kustarnikovoj rastitel’nost’yu (Development of requirements for multispectral Earth remote sensing data in the task of assessing the overgrowth of arable lands with trees and shrubs), Komp’yuternaya optika, 2019, Vol. 43, No 5, pp. 846–856.

Dmitriev E. V., Kondranin T. V., Zotov S. A., Segmentaciya prirodnyh i antropogennyh ob”ektov po panhromaticheskim sputnikovym izobrazheniyam s ispol’zovaniem statisticheskih teksturnyh priznakov (Segmentation of natural and anthropogenic objects from panchromatic satellite images using statistical texture features), Avtometriya, 2022, Vol. 58, No 2, pp. 69–84.

Fedotova E. V., Zarechneva A. I., Prostranstvenno-vremennaya dinamika vspyshki massovogo razmnozheniya sibirskogo shelkopryada v temnohvojnyh drevostoyah Gornogo Altaya (Spatial-temporal dinamics of siberian silkmoth outbreak in dark needle coniferous forest in Altay Mountains), Zhurnal Sibirskogo federal’nogo universiteta. Seriya: Tekhnika i tekhnologii, 2017, Vol. 10, No 6, pp. 747–757.

Gomez C., Wulder M., Montes F., Delgado J., Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART), Remote Sensing, 2012, Vol. 4, pp. 135–159, DOI: 10.3390/rs4010135

Kavelenova L. M., Korchikov E. S., Prohorova N. V., Terent’eva D. A., Fedoseev V. A., K  vozmozhnostyam obnaruzheniya i ocenki sostoyaniya lesopolos na osnove kompleksnogo ispol’zovaniya dannyh  DZZ i nazemnogo obsledovaniya (On the possibilities of detecting and assessing the condition of forest belts based on the integrated use of remote sensing data and ground-based surveys), IV mezhdunarodnaya konferenciya i molodezhnaya shkola «Informacionnye tekhnologii i nanotekhnologii» (ITNT-2018) (4th International Conference and Youth School «Information Technology and Nanotechnology»), Samara: Novaya tekhnika, 2018, pp. 882–891.

Knyazeva S. V., Koroleva N. V., Ejdlina S. P., Sochilova E. N., Ocenka sostoyaniya rastitel’nosti v ochage massovogo razmnozheniya sibirskogo shelkopryada po sputnikovym dannym (Health of vegetation in area of mass outbreaks of siberian moth: a satellite-based estimate), Lesovedenie, 2019, No 5, pp. 385–398.

Knyazeva S. V., Nikitina A. D., Belova E. I., Plotnikova A. S., Podol’skaya E. S., Kovganko K. A., Metodicheskie podhody k ocenke harakteristik lesov po dannym sputnikovoj s”emki sverhvysokogo prostranstvennogo razresheniya v opticheskom diapazone (Methods and approaches to the estimation of forest characteristics using the optical satellite data of very high spatial resolution), Lesovedenie, 2021, No 6, pp. 1–28.

Knyazeva S. V., Nikitina A. D., Gavrilyuk E. A., Tihonova E. V., Koroleva N. V., Ocenka biometricheskih parametrov sosnovyh drevostoev po sputnikovym dannym WorldView-3 i materialam bespilotnoj aeros”emki (Biometric parameter determination of pine stands based on WorldView-3 imagery and UAV survey), Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, Nо 6, pp. 93–107.

Komarov A. V., Ershov D. V., Tihonova E. V., Informativnost’ spektral’nyh i morfometricheskih priznakov okonnoj struktury pologa drevostoya na osnove sputnikovyh dannyh (Informativeness of spectral and morphometric characteristics of the canopy gap structure based on the remote sensing), Lesovedenie, 2021, No 3, pp. 227–239.

Lottering R., Mutanga O., Peerbhay K., Ismail R., Detecting and mapping Gonipterus scutellatus induced vegetation defoliation using WorldView-2 pan-sharpened image texture combinations and an artificial neural network, Journal of Applied Remote Sensing, 2019, Vol. 13(1), DOI: 10.1117/1.JRS.13.014513

Markov A. N., Vasil’ev A. I., Krylov A. V., Evlashkin M. A., Pestryakov A. A., Miheev A. A., Alekseevskij A. S., Osobennosti obrabotki dannyh sensora «Geoton-L1» ksmicheskogo apparata Resurs-P pri formirovanii besshovnyh sploshnyh pokrytij regionov RF (Features of data processing from the Geoton-L1 sensor of the Resurs-P spacecraft when forming seamless continuous coverage of the Russian Federation regions), Raketno-kosmicheskoe priborostroenie i informacionnye sistemy, 2020, Vol. 7, No 1, pp. 72–83.

Milovsky G. A., Ishmukhametova V. T., Aparin A. D., Primenenie kosmicheskoj s”emki vysokogo razresheniya pri poiskah pribrezhnyh rossypej i mestorozhdenij uglevodorodov v severnyh moryah Rossii (Using high resolution space survey in searching for coastal springs and deposits of hydrocarbons in the northern seas of Russia), Issledovanie Zemli iz kosmosa, 2021, No 6, pp. 74–82.

Nauchnyj centr operativnogo monitoringa Zemli AO «Rossijskie kosmicheskie sistemy» (Scientific Center for Operational Monitoring of the Earth of JSC Russian Space Systems, NC OMZ), URL: https://ntsomz.ru/ka_resurs_p_4_5/ (October 07, 2025)

Nikitina A. D., Knyazeva S. V., Koroleva N. V., Gavrilyuk E. A., Ejdlina S. P., Primenenie metoda porogovoj segmentacii izobrazhenij dlya opredeleniya parametrov drevesnoj rastitel’nosti po sputnikovym dannym sverhvysokogo prostranstvennogo razresheniya (Application of image thresholding method to determine tree vegetation parameters from ultra-high spatial resolution satellite data), Mezhdunarodnaya nauchno-prakticheskaya konferenciya «Geomatika: obrazovanie, teoriya i praktika», posvyashchennaya 50-letiyu kafedry geodezii i kosmoaerokartografii i 85-letiyu fakul’teta geografii i geoinformatiki BGU (International Scientific and Practical Conference «Geomatics: Education, Theory, and Practice»), Sb. statej. RB, Minsk, 2019. pp. 114–118.

Peshkun A. A., Sozdanie trekhmernyh modelej mestnosti s ispol’zovaniem materialov s”emki kosmicheskogo apparata tipa «Resurs-P» (Creating of 3D surface models using «Resurs-P» spacecraft images), Raketno-kosmicheskoe priborostroenie i informacionnye sistemy, 2016, Vol. 3, No 1, pp. 28–33.

Sibiya B., Lottering R., Odindi J., Utility of texture combinations computed from fused WorldView-2 imagery in discriminating commercial forest species, Geocarto international, 2022, Vol. 37, No 23, pp. 6915–6931, DOI: 10.1080/10106049.2021.195231623

Terekhov A. G., Makarenko N. G., Pak I. T., Avtomaticheskij algoritm klassifikacii snimkov QuickBird v zadache ocenki polnoty lesa (Automatic classification algorithm of quick bird images in the problem of evaluating of forest completeness), Komp’yuternaya optika, 2014, Vol. 38, No 3, pp. 580–583.

Varlamova A. A., Denisova A. Yu., Sergeev V. V., Informacionnaya tekhnologiya obrabotki dannyh DZZ dlya ocenki arealov rastenij (Information technology for processing remote sensing data for assessing plant ranges), Komp’yuternaya optika, 2018, Vol. 42, No 5, pp. 864–876.

Wang W., Yao X., Yao X., Tian Y., Liu X., Ni J., Cao W., Zhu Y., Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat, Field Crops Research, 2012, Vol. 129, pp. 90–98, DOI: 10.1016/j.fcr.2012.01.014

Yurovskaya M. V., Kudryavtsev V. N., Stanichny S. V., Vosstanovlenie kinematicheskih harakteristik poverhnostnogo volneniya i batimetrii po mnogokanal’nym opticheskim snimkam kompleksa «Geoton-L1» na sputnike «Resurs-P» (Reconstruction of surface wave kinematic characteristics and bathymetry from Geoton-L1 multichannel optical images from Resurs-P satellite), Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No 2, pp. 218–226.

Zhirin V. M., Knyazeva S. V., Ejdlina S. P., Ocenka biometricheskih parametrov nasazhdenij po izobrazheniyam mezhkronovogo prostranstva na kosmicheskih snimkah sverhvysokogo razresheniya (Estimation of linkages between biometric indexes of forests and pattern of canopy spaces on super-high resolution satellite images), Lesovedenie, 2018, No 3, pp. 163–177.