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This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL.

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Abstract:Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual tree detection approach, using reFLex software, in the evaluation of forest variables. The accuracy assessment was conducted in a selected type of multilayered deciduous forest in southern Italy. Airborne laser scanning data were taken with a YellowScan Mapper scanner at an average height of 150 m. Point density reached 30 echoes per m 2 , but most points belonged to the first echo. The ground reference data contained the measured positions and dimensions of 445 trees. Individual tree-detection rates were 66% for dominant, 48% for codominant, 18% for intermediate, and 5% for suppressed trees. Relative root mean square error for tree height, diameter, and volume reached 8.2%, 21.8%, and 45.7%, respectively. All remote-based tree variables were strongly correlated with the ground data (R 2 = 0.71-0.79). At the stand-level, the results show that differences ranged between 4% and 17% for stand height and 22% and 40% for stand diameter. The total growing stock differed by −43% from the ground reference data, and the ratios were 64% for dominant, 58% for codominant, 36% for intermediate, and 16% for suppressed trees.

Airborne laser scanning (ALS) has recently gained increasing attention in forestry, as ALS data may facilitate the efficient assessment of forest inventory attributes and ecological indicators related to forest stand structure. This paper presents a novel workflow for individual tree detection and tree crown delineation using ALS data. The developed point-based approach included several tree allometry rules on permissible tree heights and crown dimensions to increase the likelihood of detecting the actual tree profiles. The accuracy of the method was assessed in a heterogeneous forest with a complex stand structure in Slovakia (Central Europe). ALS measurements were taken using a RIEGL Q680i scanner at 700 m of height with a point density of 20 echoes per m 2 . The ground reference data included the measured positions and dimensions of 1332 trees in nine plots distributed across the region. We found that the number of individual trees detected by the algorithm using ALS data was systematically underestimated by 34 ± 15% relative to the reference data. The delineated crown coverage was underestimated by 2 ± 6% as well, but the latter difference was not statistically significant (p>0.05).

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