Non-invasive environmental monitoring of the coniferous forests using statistical analysis and data modelling

Lemenkova Polina

doi:10.5937/ror2501035L

Authors

Lemenkova Polina University of Bologna, Department of Biological, Geological and Environmental Sciences, Bologna, Italy Author

DOI:

https://doi.org/10.5937/ror2501035L

Keywords:

sustainable development, climate change, ecology, environmental monitoring

Abstract

Climate plays a pivotal role in construction of relationships between coniferous forest health and water balance for efficient biosynthesis under changing meteorological variables. Here we identify that the age of the forest (young <30 years old, and old >200 years old) confers an improved ecophysiology for the maintenance of water balance through the response of trees to weather conditions (precipitation, temperature and water balance in different seasons). Global climate change, of the rise in temperatures, has an impact on the environment of mountain ecosystems in the Alps. Subalpine forests enhance partitioning precipitation inputs, with interception processes in the tree canopy determining the stability of the proportion of water reaching the ground and its quantity in the form of evaporation. These processes support global improvements in forest ecosystems at the catchment scale, as interception is influenced by the structure and density of the tree canopy and the presence of epiphytes. This study revealed that coniferous forests (spruce, fir and pine) have a significant influence on how much water is retained and discharged in the soil and plants. Interception in dense subalpine forests can account for a significant proportion of precipitation. Data analysis using Python-based modeling revealed that forest age increases biosynthesis by enhancing water fluxes. This study highlights the significance of uncovering hidden climate-environment determinants leading to improved forest hydrology and ecophysiology to enhance the biosynthesis and water balance in coniferous forests of the Alps.

References

Akkala A., Boubrahimi S.F., Hamdi S.M., Hosseinzadeh P., Nassar A., Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin. Hydrology, 12 (3), 2025, 60

https://doi.org/10.3390/hydrology12030060

Alnmr A., Ray R., Alzawi M.O. A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils. Applied Sciences, 14 (4), 2024, 1411

https://doi.org/10.3390/app14041411

Beswick K. M., Hargreaves K., Gallagher M. W., Choularton T. W., Fowler D., Size-resolved measurements of cloud droplet deposition velocity to a forest canopy using an eddy correlation technique. Quarterly Journal of the Royal Meteorological Society, 117, 1991, 623-645

https://doi.org/10.1256/smsqj.49909

Breuer L., Eckhardt K., Frede H.-G., Plant parameter values for models in temperate climates. Ecological Modelling, 169, 2-3, 2003, 237-293

https://doi.org/10.1016/S0304-3800(03)00274-6

Brunner I., Herzog C., Dawes M., Arend M., Sperisen C., How tree roots respond to drought. Frontiers in Plant Science, 6, 2015, 1-16

https://doi.org/10.3389/fpls.2015.00547

Caldwell P.V., Miniat C.F., Elliott K.J., Swank W.T., Brantley S.T., Laseter S.H., Declining water yield from forested mountain watersheds in response to climate change and forest mesophication. Global Change Biology, 22, 2016, 2997-3012

https://doi.org/10.1111/gcb.13309

Cattivelli V., Climate Adaptation Strategies and Associated Governance Structures in Mountain Areas. The Case of the Alpine Regions. Sustainability, 13(5), 2021, 2810

https://doi.org/10.3390/su13052810

Cattivelli V., Macro-Regional Strategies, Climate Policies and Regional Climatic Governance in the Alps. Climate, 11(2), 2023, 37

https://doi.org/10.3390/cli11020037

Chandel A. K., Khot L. R., Stöckle C. O., Kalcsits L., Mantle S., Rathnayake A. P., Peters T. R., Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data. AgriEngineering, 7(5), 2025, 154

https://doi.org/10.3390/agriengineering7050154

Chen J., Zhang H., Liu Z., Che M., Chen B., Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements. Remote Sensing, 6(4), 2014, 3321-3348

https://doi.org/10.3390/rs6043321

Chen Z., Hartmann A., Wagener T., Goldscheider N., Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions. Hydrology and Earth System Sciences, 22 (7), 2018, 3807-3823

https://doi.org/10.5194/hess-22-3807-2018

Dohnal M., Černý T., Votrubová J., Tesař M., Rainfall interception and spatial variability of throughfall in spruce stand. Journal of Hydrology and Hydromechanics, 62(4), 2014, 277-284

https://doi.org/10.2478/johh-2014-0037

Evrendilek F., Karakaya N., Aslan G., Ertekin C., Using Eddy Covariance Sensors to Quantify Carbon Metabolism of Peatlands: A Case Study in Turkey. Sensors, 11(1), 2011, 522-538

https://doi.org/10.3390/s110100522

Feng M., Li Y., Xu L, Zhang T., Basin Ecological Zoning Based on Supply-Demand Assessment and Matching of Green Infrastructure: A Case Study of the Jialing River Basin. Forests, 16(4), 2025, 561

https://doi.org/10.3390/f16040561

Gerula D., Gąbka J., The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas. Applied Sciences, 15(8), 2025, 4497

https://doi.org/10.3390/app15084497

Kandel B., Bhattacharjee J., Net Radiation Drives Evapotranspiration Dynamics in a Bottomland Hardwood Forest in the Southeastern United States: Insights from Multi-Modeling Approaches. Atmosphere, 15(5), 2024, 527

https://doi.org/10.3390/atmos15050527

Karinou F., Agathangelidis I., Cartalis C., Assessing the Combined Impact of Land Surface Temperature and Droughts to Heatwaves over Europe Between 2003 and 2023. Remote Sensing, 17(9), 2025, 1655

https://doi.org/10.3390/rs17091655

Klaučo M, Gregorová B, Stankov U, Marković V, Lemenkova P., Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area. Central European Journal of Geography, 5, 2013, 28-42

https://doi.org/10.2478/s13533-012-0120-0

Klaučo M., Gregorová B., Koleda P., Stankov U., Marković V., Lemenkova P., Land Planning as a Support for Sustainable Development Based on Tourism: A Case Study of Slovak Rural Region, Environmental Engineering and Management Journal, 16(2), 2017, 449-458

https://doi.org/10.30638/eemj.2017.045

Klemm O., Wrzesinsky T., Fog deposition fluxes of water and ions to a mountainous site in Central Europe, Tellus B, Chemical and Physical Meteorology, 59(4), 2007, 705-714

https://doi.org/10.1111/j.1600-0889.2007.00287.x

Kulshreshtha K., Narayanan S. H. K., Bessac J., MacIntyre K., Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C†. Optimization Methods and Software, 33(4-6), 2018, 1173-1191

https://doi.org/10.1080/10556788.2018.1425861

Kustura K., Conti D., Sammer M., Riffler M., Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities. Remote Sensing, 17(2), 2025, 318

https://doi.org/10.3390/rs17020318

Kupec P., Deutscher J., Futter, M., Longer Growing Seasons Cause Hydrological Regime Shifts in Central European Forests. Forests, 12(12), 2021, 1656

https://doi.org/10.3390/f12121656

Lemenkova P. Generic Mapping Tools and Matplotlib Package of Python for Geospatial Data Analysis in Marine Geology. International Journal of Environment and Geoinformatics, 6(3), 2019a, 225-237

https://doi.org/10.30897/ijegeo.567343

Lemenkova P., Processing oceanographic data by python libraries numpy, scipy and pandas. Aquatic Research, 2(2), 2019b, 73-91

https://doi.org/10.3153/AR19009

Lemenkova P., Statistical analysis of the Mariana Trench geomorphology using R programming language. Geodesy and Cartography, 45(2), 2019c, 57-84

https://doi.org/10.3846/gac.2019.3785

Lemenkova P., Mapping environmental and climate variations by GMT: A case of Zambia, Central Africa. Zemljište i biljka,70(1), 2021a, 117-36

https://doi.org/10.5937/ZemBilj2101117L

Lemenkova P., Comparative analysis of climate and topography in Chaco and Oriental, Paraguay, Caderno de Geografia, 31(66), 2021b, 865-888

https://doi.org/10.5752/P.2318-2962.2021v31n66p865

Lemenkova P., Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate, ISPRS International Journal of Geo-Information, 11(9), 2022a, 473

https://doi.org/10.3390/ijgi11090473

Lemenkova P., Evapotranspiration, vapour pressure and climatic water deficit in Ethiopia mapped using GMT and TerraClimate dataset, Journal of Water and Land Development, 54(7-9), 2022b, 201-209

https://doi.org/10.24425/jwld.2022.141573

Lemenkova P., Mapping Wetlands of Kenya Using Geographic Resources Analysis Support System (GRASS GIS) with Remote Sensing Data. Transylvanian Review of Systematical and Ecological Research, 25(2), 2023, 1-18

https://doi.org/10.2478/trser-2023-0008

Lemenkova P., Mapping Woodlands in Angola, Tropical Africa: Calculation of Vegetation Indices From Remote Sensing Data, The Journal Agriculture and Forestry, 70(3), 2024a, 185-202

https://doi.org/10.17707/AgricultForest.70.3.13

Lemenkova P., Support Vector Machine Algorithm for Mapping Land Cover Dynamics in Senegal, West Africa, Using Earth Observation Data. Earth, 5(3), 2024b, 420-462

https://doi.org/10.3390/earth5030024

Lemenkova P., Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python, Examples and Counterexamples, 7, 2025a, 100180

https://doi.org/10.1016/j.exco.2025.100180

Lemenkova P., Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS. Geomatics, 5(1), 2025b, 5

https://doi.org/10.3390/geomatics5010005

Lemenkova P., Data integration for environmental mapping and monitoring in South Tyrol. Western Balkan Journal of Agricultural Economics and Rural Development, 7(1), 2025c, 37-51

https://doi.org/10.5937/WBJAE2501037L

Lemenkova P., Python-based statistical analysis of the ecological variables in the Italian Alps, Journal of Process Management and New Technologies 13, 1-2, 2025d, 102-112

https://doi.org/10.5937/jpmnt13-57672

Li H., Rex J., Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data. Remote Sensing, 17(5), 2025, 930

https://doi.org/10.3390/rs17050930

Li X., An F., Wang Y., Gong M., Xu H., Zheng B., Dong L., Yu R. Comparative Analysis of Drought-Driven Water-Use Strategies in Mangroves and Forests. Forests, 16(3), 2025, 396

https://doi.org/10.3390/f16030396

Liu J., Gao G., Zhang B., Effect of Shrub Components on Soil Water and Its Response to Precipitation at Different Time Scales in the Loess Plateau. International Journal of Environmental Research and Public Health, 20(6), 2023, 4722

https://doi.org/10.3390/ijerph20064722

Liu J., Zhao J., He J., Qu J., Xing Y., Du R., Chen S., Tang X., Wang L., Yue C., Spatiotemporal Trend and Influencing Factors of Surface Soil Moisture in Eurasian Drylands over the Past Four Decades, Forests, 16(4), 2025, 589

https://doi.org/10.3390/f16040589

Los S. O., Pollack N. H., Parris M. T., Collatz G. J., Tucker C. J., Sellers P. J., Malmström C. M., DeFries R. S., Bounoua L., Dazlich, D. A., A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data. Journal of Hydrometeorology, 1(2), 2000, 183-199

https://doi.org/10.1175/1525-7541(2000)001<0183:AGYBLS>2.0.CO;2

Los S. O., Collatz G. J., Bounoua L., Sellers P. J., Tucker C. J., Global Interannual Variations in Sea Surface Temperature and Land Surface Vegetation, Air Temperature, and Precipitation. Journal of Climate, 14(7), 2001, 1535-1549

https://doi.org/10.1175/1520-0442(2001)014<1535:GIVISS>2.0.CO;2

Menzel A. Trends in phenological phases in Europe between 1951 and 1996. International Journal of Biometeorology, 44, 2000, 76-81

https://doi.org/10.1007/s004840000054

Nikić Z., Letić L., Nikolić V., Formation of useful waters on the Stara Planina Mountain, in the area Visok Kraj, Glasnik Šumarskog fakulteta, 105, 2012, 139-56

https://doi.org/10.2298/GSF110919001N

Ranković N, Nedeljković J, Zlatić M, Stanišić M, Nonić D., Trends in the extent of damages from natural hazards in forests in Serbia and the influence of temperature and precipitation. Glasnik Šumarskog fakulteta, 114, 2016, 201-18

https://doi.org/10.2298/GSF1614201R

Ratknić T, Ratknić M, Vukadinović L., The regional climate model (REG-IN) for forecasting the adaptivity of forest ecosystems in Belgrade. Sustainable Forestry: Collection, 79-80, 2019, 127-39

https://doi.org/10.5937/SustFor1979127R

Scavotto N., Siegert C. M., Alexander H. D., Varner J. M., Leaf Water Storage Capacity Among Eight US Hardwood Tree Species: Differences in Seasonality and Methodology. Hydrology, 12(2), 2025, 40

https://doi.org/10.3390/hydrology12020040

Schermer M., Stotten R., Strasser U., Meißl G., Marke T., Förster K., Formayer H., The Role of Transdisciplinary Research for Agricultural Climate Change Adaptation Strategies. Agronomy, 8(11), 2018, 237

https://doi.org/10.3390/agronomy8110237

She B., Hu T., Zhu X., Bao S., Bridging open source tools and Geoportals for interactive spatial data analytics. Geo-Spatial Information Science, 22(3), 2019, 185-192

https://doi.org/10.1080/10095020.2019.1645497

Skalova V., Dohnal M., Votrubova J., Vogel T., Sanda, M., Inter-annual variability of catchment water balance in a montane spruce forest. Hydrological Sciences Journal, 67(10), 2022, 1546-1560

https://doi.org/10.1080/02626667.2022.2093643

Stajić S., Čokeša V., Eremija S., Miletić Z., Rakonjac L., Martać N., et al. Floristic diversity of artificially established stands of different coniferous species in the area of Kosmaj (Serbia). Sustainable Forestry: Collection, 87-88, 2023, 47-56

https://doi.org/10.5937/SustFor2388047S

Studer S., Stöckli R., Appenzeller C., Vidale P.L., A comparative study of satellite and ground-based phenology. International Journal of Biometeorology, 51, 2007, 405-414

https://doi.org/10.1007/s00484-006-0080-5

Swidrak I., Schuster R., Oberhuber, W., Comparing growth phenology of co-occurring deciduous and evergreen conifers exposed to drought. Flora - Morphology, Distribution, Functional Ecology of Plants, 208, 2013, 609-617

https://doi.org/10.1016/j.flora.2013.09.004

Tanovski V., Matović B., Kesić L., Stojanović D. B., A review of the influence of climate change on coniferous forests in the Balkan peninsula. Topola, 210, 2022, 41-64

https://doi.org/10.5937/topola2210041T

Thorne R., Arain M. A., Influence of Low Frequency Variability on Climate and Carbon Fluxes in a Temperate Pine Forest in Eastern Canada. Forests, 6(8), 2015, 2762-2784

https://doi.org/10.3390/f6082762

Uehara Y., Kume, A., Canopy Rainfall Interception and Fog Capture by Pinus pumila Regal at Mt. Tateyama in the Northern Japan Alps, Japan. Arctic, Antarctic, and Alpine Research, 44(1), 2012, 143-150

https://doi.org/10.1657/1938-4246-44.1.143

van Rossum, G. Python tutorial, Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, 1995

Vázquez-Luna M., Ellis E. A., Navarro-Martínez M. A., Cerdán-Cabrera C. R., Ortiz-Ceballos G. C., Evaluating the Spatial Relationships Between Tree Cover and Regional Temperature and Precipitation of the Yucatán Peninsula Applying Spatial Autoregressive Models. Land, 14(5), 2025, 943

https://doi.org/10.3390/land14050943

Wang L, Wu B, Zhu W, Elnashar A, Yan N, Ma Z., Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units. Remote Sensing. 17(7), 2025a, 1201

https://doi.org/10.3390/rs17071201

Wang J., Wang S., Li Y., Ding S., Wei Z., Shi A., Yang D., Distribution Pattern and Change Prediction of Luprops orientalis (Coleoptera: Tenebrionidae) Suitable Area in East Asia Under Climate Change. Insects, 16(6), 2025b, 626

https://doi.org/10.3390/insects16060626

Węgrzyn M. H., Fałowska P., Alzayany K., Waszkiewicz K., Dziurowicz P., Wietrzyk-Pełka P., Seasonal Changes in the Photosynthetic Activity of Terrestrial Lichens and Mosses in the Lichen Scots Pine Forest Habitat. Diversity, 13(12), 2021, 642

https://doi.org/10.3390/d13120642

Weng F., Li X., Xie Y., Xu Z., Ding F, Ding Z, Zheng Y. Study on Multidimensional Perception of National Forest Village Landscape Based on Digital Footprint Support-Anhui Xidi Village as an Example. Forests, 14(12), 2023, 2345

https://doi.org/10.3390/f14122345

White P., Powell S., Code-Literacy for GIS Librarians: A Discussion of Languages, Use Cases, and Competencies. Journal of Map and Geography Libraries, 15(1), 2019, 45-67

https://doi.org/10.1080/15420353.2019.1660754

Yu W., Sheng L., Wang X., Tang X., Yuan J., Luo W., Soil Microbial Carbon Use Efficiency in Natural Terrestrial Ecosystems. Biology, 14(4), 2025, 348

https://doi.org/10.3390/biology14040348

Zhang L., Tian J., He H., Ren X., Sun X., Yu G., Lu Q., Lv, L., Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing, 7(9), 2015, 11183-11201

https://doi.org/10.3390/rs70911183

Zhang Q., Feng Q., Su Y., Jian C., Evapotranspiration Partitioning of the Populus euphratica Forest Ecosystem in the Drylands of Northwestern China. Plants. 14(5), 2025, 680

https://doi.org/10.3390/plants14050680

Zhu H., Lin A., Wang L., Xia Y., Zou L., Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data. Remote Sensing, 8(5), 2016, 395

https://doi.org/10.3390/rs8050395