Országos, nagyfelbontású ökoszisztéma-alaptérkép: módszertan, validáció és felhasználási lehetőségek

Tanács, Eszter; Belényesi, Márta; Lehoczki, Róbert; Pataki, Róbert; Petrik, Ottó; Standovár, Tibor; Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Molnár, Zsolt; Bede‐Fazekas, Ákos; Fodor, Lívia Kisné; Varga, Ildikó; Zsembery, Zita; Maucha, G.

doi:10.20332/tvk-jnatconserv.2019.25.34

Cited by 16 publications

(10 citation statements)

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“…Composite grassland simulation was performed using the so-called NÖSZTÉP land cover database (Tanács et al 2019). By using the NÖSZTÉP data it was possible to distinguish between productive and less productive grassland sites in Hungary.…”

Section: Model Simulationsmentioning

confidence: 99%

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

Fodor

Pásztor

Szabó

et al. 2021

International Journal of Digital Earth

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Gridded model assessments require at least one climatic and one soil database for carrying out the simulations. There are several parallel soil and climate database development projects that provide sufficient, albeit considerably different, observation based input data for crop model based impact studies. The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases, respectively covering an area of nearly 100.000 km 2 with 1104 grid cells. Spatial, temporal, climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations. The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes. Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run. Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations.

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Section: Model Simulationsmentioning

confidence: 99%

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

Fodor

Pásztor

Szabó

et al. 2021

International Journal of Digital Earth

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“…We separated trunks less than 70 cm in diameter and those over 70 cm in diameter into classes during the photo interpretation. Trees with about 60-70 cm trunks are commonly classified as a 'large canopy size' class of trees, while the larger size trees are considered 'very large' [69][70][71]. No fallen tree with a trunk diameter wider than 130 cm was found in the area.…”

Section: Complex Evaluation Optionsmentioning

confidence: 99%

Toward a High Spatial Resolution Aerial Monitoring Network for Nature Conservation—How Can Remote Sensing Help Protect Natural Areas?

Bakó¹,

Molnár²,

Bakk³

et al. 2021

Sustainability

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Aerial surveys have always significantly contributed to the accurate mapping of certain geographical phenomena. Remote sensing opened up new perspectives in nature monitoring with state-of-the-art technical solutions using modern onboard recording equipment. We developed the technical background and the methodology that supports detailed and cost-effective monitoring of a network of natural areas, thereby detecting temporal changes in the spatial pattern of land cover, species, biodiversity, and other natural features. In this article, we share our experiences of the technical background, geometric accuracy and results of comparisons with selected Copernicus Land Monitoring products and an Ecosystem Map based on the testing of our methodology at 25 sites in Hungary. We combined a high-spatial-resolution aerial remote sensing service with field studies to support an efficient nature conservation monitoring network at 25 permanent sites. By analyzing annually (or more frequently) orthophotos taken with a range of 0.5–5 cm spatial resolution and 3D surface models of aerial surveys, it is possible to map the upper canopy of vegetation species. Furthermore, it allows us to accurately follow the changes in the dynamics at the forest edge and upper canopy, or the changes in species’ dominance in meadows. Additionally, spatial data obtained from aerial surveys and field studies can expand the knowledge base of the High-Resolution Aerial Monitoring Network (HRAMN) and support conservation and restoration management. A well-conducted high-resolution survey can reveal the impacts of land interventions and habitat regeneration. By building the HRAMN network, nature conservation could have an up-to-date database that could prompt legal processes, establish protection designation procedures and make environmental habitat management more cost-effective. Landscape protection could also utilize the services of HRAMN in planning and risk reduction interventions through more reliable inputs to environmental models.

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“…Digital soil mapping (DSM), which has become a successful sub-discipline of soil science with an active research output [10], aims to provide spatial soil information for a wide range of studies, such as precision agriculture [11,12], hydrology [13][14][15], environmental sciences [16,17], conservation biology [18,19] or spatial planning [20,21]. For this purpose, geostatistical techniques are widely used, which have been complemented by machine learning algorithms in the past decade.…”

Section: Introductionmentioning

confidence: 99%

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

et al. 2020

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Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

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Országos, nagyfelbontású ökoszisztéma-alaptérkép: módszertan, validáció és felhasználási lehetőségek

Cited by 16 publications

References 1 publication

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

Toward a High Spatial Resolution Aerial Monitoring Network for Nature Conservation—How Can Remote Sensing Help Protect Natural Areas?

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

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