Hidden Forest in Non-Forest Land: A Remote Sensing-Based Mapping Case in Lithuania.
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| Title: | Hidden Forest in Non-Forest Land: A Remote Sensing-Based Mapping Case in Lithuania. |
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| Authors: | Papartė, Monika1 (AUTHOR) monika.paparte@vdu.lt, Jonikavičius, Donatas1,2 (AUTHOR), Mozgeris, Gintautas1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1665. 31p. |
| Subjects: | LIDAR, Forest mapping, Forest surveys, Woody plants, Forest reserves, Remote sensing |
| Geographic Terms: | Lithuania |
| Abstract: | Highlights: What are the main findings? A multi-source remote sensing workflow integrating airborne LiDAR, Sentinel-2 imagery, historical orthophotos, and ancillary geospatial datasets enabled nationwide identification of forest-eligible areas (FEAs) outside officially registered forest land in Lithuania. A total of 111,754.4 ha of FEAs were identified, including 76,204.6 ha meeting the minimum age criterion for forest classification; the LiDAR-based approach achieved the highest overall accuracy (91.5%), while RS–LPIS integration increased precision with the potential to reach 97.1%. What are the implications of the main findings? Substantial woody vegetation, potentially meeting legal forest criteria, remains outside official forest statistics. The proposed framework supports wall-to-wall identification of candidate forest-eligible areas for forest inventory and monitoring. Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A multi-source remote sensing workflow integrating airborne LiDAR, Sentinel-2 imagery, historical orthophotos, and ancillary geospatial datasets enabled nationwide identification of forest-eligible areas (FEAs) outside officially registered forest land in Lithuania. A total of 111,754.4 ha of FEAs were identified, including 76,204.6 ha meeting the minimum age criterion for forest classification; the LiDAR-based approach achieved the highest overall accuracy (91.5%), while RS–LPIS integration increased precision with the potential to reach 97.1%. What are the implications of the main findings? Substantial woody vegetation, potentially meeting legal forest criteria, remains outside official forest statistics. The proposed framework supports wall-to-wall identification of candidate forest-eligible areas for forest inventory and monitoring. Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101665 |
