Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques

Frate, Ludovico; Carranza, María Laura; Garfì, Vittorio; Febbraro,; Tonti, Daniela; Marchetti, Marco; Ottaviano, Marco; Santopuoli, Giovanni; Chirici, Gherardo

doi:10.3832/ifor1529-008

Cited by 17 publications

(11 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forest attributes related to deadwood were also quantified within the same circular plots (Lombardi et al 2008). Typological-structural parameters including forest category, forest management and stand age were attributed according to the regional forest type and age maps (Vizzarri et al 2015, Frate et al 2016. Numerical covariates were standardized and sub-selected to avoid multicollinearity, considering a Variance Inflation Factor lower or equal to 10 ( Zuur et al 2010).…”

Section: Forest Parameters Estimation and Selectionmentioning

confidence: 99%

Effect of imperfect detection on the estimation of niche overlap between two forest dormice

Paniccia¹,

Febbraro²,

Frate³

et al. 2018

iForest

Self Cite

View full text Add to dashboard Cite

Quantification of niche overlap represents an important topic in several aspects of ecology and conservation biology, although it could be potentially affected by imperfect detection, i.e., failure to detect a species at occupied sites. We investigate the effect of imperfect detection on niche overlap quantification in two arboreal rodents, the edible dormouse (Glis glis) and the hazel dormouse (Muscardinus avellanarius). For both species, we used Generalized Linear Mixed Models (GLMM) to estimate the occurrence probability and Occupancy Models (OM) to calculate occurrence and detection probabilities. By comparing these predictions through niche equivalency and similarity tests, we first hypothesised that methods correcting for imperfect detection (OM) provide a more reliable estimate of niche overlap than traditional presence/ absence methods (GLMM). Furthermore, we hypothesised that GLMM mainly estimate species detectability rather than actual occurrence, and that a low number of sampling replicates provokes an underestimation of species niche by GLMM. Our results highlighted that GLMM-based niche overlap yielded significant outcomes only for the equivalency test, while OM-based niche overlap reported significant outcomes for both niche equivalency and similarity tests. Moreover, GLMM occurrence probabilities and OM detectabilities were not statistically different. Lastly, GLMM predictions based on single sampling replicates were statistically different from the average occurrence probability predicted by GLMM over all replicates. We emphasized how accounting for imperfect detection can improve the statistical significance and interpretability of niche overlap estimates based on occurrence data. Under a habitat management perspective, an accurate quantification of niche overlap may provide useful information to assess the effects of different management practices on species occurrence.

show abstract

Section: Forest Parameters Estimation and Selectionmentioning

confidence: 99%

Effect of imperfect detection on the estimation of niche overlap between two forest dormice

Paniccia¹,

Febbraro²,

Frate³

et al. 2018

iForest

Self Cite

View full text Add to dashboard Cite

show abstract

“…The resultant RMSE was less than 10 years. Age has also been predicted based on a time series of remotely sensed data (Vastaranta et al 2015), optical remote sensing information and inversed yield models (Frate et al 2015) or indirectly by predicting stand development stages (Falkowski et al 2009;Weber and Boss 2009;Kane et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

et al. 2020

View full text Add to dashboard Cite

Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

show abstract

“…In the study by Maltamo et al (2009), 69 independent validation stands with an average area of 1 ha and age ranging from 0 to 126 years were used, resulting in stand level RMSE of 18.3 years (36%) for spruce. In a Mediterranean to temperate climate in central Italy, Frate et al (2015) obtained RMSE of 16 years (30%) using 305 independent validation stands with stand age ranging from 1 to 127 years and mean of 52 years. Our results on 63 independent validation stands were comparable with RMSE of 11.5 (22%).…”

Section: Discussionmentioning

confidence: 99%

“…Percentage of stands with absolute age errors below 2 years were up to 83% of all stands for multiple regression modelling and up to 98% of all stands for the best artificial neural network. In a study in central Italy comprising 128,402 ha forest in various growing conditions, Frate et al (2015) used multispectral satellite imagery and 304 field plots to first model timber volume using the kNN approach, and subsequently used inverted yield models to predict forest age. On 305 independent validation stands covering 3137 ha and stand age from 1 to 127 years with a mean of 52 years, they obtained forest age estimates with RMSE of 16 years (30%).…”

Section: Introductionmentioning

confidence: 99%

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

et al. 2020

View full text Add to dashboard Cite

Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

show abstract

Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques

Cited by 17 publications

References 53 publications

Effect of imperfect detection on the estimation of niche overlap between two forest dormice

Effect of imperfect detection on the estimation of niche overlap between two forest dormice

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

Contact Info

Product

Resources

About