Woody Above-Ground Biomass Estimation on Abandoned Agriculture Land Using Sentinel-1 and Sentinel-2 Data

Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R2 = 0.84 and RMSE = 41.2 t·ha−1 (35.1%), parametrized for an AGB range of 4 to 350 t·ha−1. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub–tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition

Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular

A Review of the Application of Remote Sensing Data for Abandoned Agricultural Land Identification with Focus on Central and Eastern Europe

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

Evaluating five forest models using multi-decadal inventory data from mountain forests

Forest ecosystem models, being widespread science tools and used for forest management decision support are usually evaluated individually against field data sets, while model intercomparison and joint evaluation studies are rare. We tested five forest models according to a harmonized protocol against data from nine forest compartments in the Snĕžnik region, in Slovenia. The suite of models included stand- and landscape-scale, empirical- and process-based models used across Europe. The test dataset originated from inventory data covering 50 years (tree measurements 1963, 1983 and 2013) and included annual harvesting records at tree level. Uncertainties in data and forest conditions were considered by defining 12 scenarios varying initial regeneration, browsing pressure and harvest modalities. We evaluated the models` ability to initialize forest conditions accurately, whether management interventions could be implemented based on harvest records, and how well basal area and diameter structure could be predicted. Simulation results for basal area development showed good to satisfactory performance for all models, at which SAMSARA2, SIBYLA and PICUS showed the best agreement. Comparison of simulated and observed diameter distributions showed good performance of ForClim, PICUS, SAMSARA2 and SIBYLA. Model output variability was between 6% and 24%, indicating the relevance to consider uncertainties that can be attributed to specific sources. There was no clear hierarchy between more empirical or more process-based models regarding accuracy of stand development projections. The cohort-based landscape model LandClim showed the lowest stand-level accuracy and scenario sensitivity, but results nevertheless qualified it for complementary application at landscape scale. Within individual-based models, spatially explicit models seemed to be more suitable for heterogeneous mixed mountain forests. The findings demonstrated the usefulness of inventory datasets for model testing and intercomparison.ISSN:0304-3800ISSN:1872-7026ISSN:0167-889

Evaluating five forest models using multi-decadal inventory data from mountain forests

Forest ecosystem models, being widespread science tools and used for forest management decision support are usually evaluated individually against field data sets, while model intercomparison and joint evaluation studies are rare. We tested five forest models according to a harmonized protocol against data from nine forest compartments in the Sn%žnik region, in Slovenia. The suite of models included stand- and landscape-scale, empirical- and process-based models used across Europe. The test dataset originated from inventory data covering 50 years (tree measurements 1963, 1983 and 2013) and included annual harvesting records at tree level. Uncertainties in data and forest conditions were considered by defining 12 scenarios varying initial regeneration, browsing pressure and harvest modalities. We evaluated the models` ability to initialize forest conditions accurately, whether management interventions could be implemented based on harvest records, and how well basal area and diameter structure could be predicted. Simulation results for basal area development showed good to satisfactory performance for all models, at which SAMSARA2, SIBYLA and PICUS showed the best agreement. Comparison of simulated and observed diameter distributions showed good performance of ForClim, PICUS, SAMSARA2 and SIBYLA. Model output variability was between 6% and 24%, indicating the relevance to consider uncertainties that can be attributed to specific sources. There was no clear hierarchy between more empirical or more process-based models regarding accuracy of stand development projections. The cohort-based landscape model LandClim showed the lowest stand-level accuracy and scenario sensitivity, but results nevertheless qualified it for complementary application at landscape scale. Within individual-based models, spatially explicit models seemed to be more suitable for heterogeneous mixed mountain forests. The findings demonstrated the usefulness of inventory datasets for model testing and intercomparison

Corrigendum to ’evaluating five forest models using multi-decadal inventory data from mountain forests’

ISSN:0304-3800ISSN:1872-7026ISSN:0167-889

Structure-Guided Design of a Potent and Specific Inhibitor against the Genomic Mutator APOBEC3A

Nucleic acid structure plays a critical role in governing the selectivity of DNA- and RNA-modifying enzymes. In the case of the APOBEC3 family of cytidine deaminases, these enzymes catalyze the conversion of cytosine (C) to uracil (U) in single-stranded DNA, primarily in the context of innate immunity. DNA deamination can also have pathological consequences, accelerating the evolution of viral genomes or, when the host genome is targeted by either APOBEC3A (A3A) or APOBEC3B (A3B), promoting tumor evolution leading to worse patient prognosis and chemotherapeutic resistance. For A3A, nucleic acid secondary structure has emerged as a critical determinant of substrate targeting, with a predilection for DNA that can form stem loop hairpins. Here, we report the development of a specific nanomolar-level, nucleic acid-based inhibitor of A3A. Our strategy relies on embedding the nucleobase 5-methylzebularine, a mechanism-based inhibitor, into a DNA dumbbell structure, which mimics the ideal substrate secondary structure for A3A. Structure–activity relationship studies using a panel of diverse inhibitors reveal a critical role for the stem and position of the inhibitor moiety in achieving potent inhibition. Moreover, we demonstrate that DNA dumbbell inhibitors, but not nonstructured inhibitors, show specificity against A3A relative to the closely related catalytic domain of A3B. Overall, our work demonstrates the feasibility of leveraging secondary structural preferences in inhibitor design, offering a blueprint for further development of modulators of DNA-modifying enzymes and potential therapeutics to circumvent APOBEC-driven viral and tumor evolution