Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

Bollandsås, Ole Martin; Ørka, Hans Ole; Dalponte, Michele; Gobakken, Terje; Næsset, Erik

doi:10.3390/rs11091020

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…A range of forest inventory applications may benefit from change classification. First, recent studies have shown that canopy height growth as depicted by bitemporal ALS data enables the estimation of forest productivity [31][32][33]. Forest productivity is among the most essential variables in forest management and is expensive to record in field surveys.…”

Section: Introductionmentioning

confidence: 99%

Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data

et al. 2019

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Changes in forest areas have great impact on a range of ecosystem functions, and monitoring forest change across different spatial and temporal resolutions is a central task in forestry. At the spatial scales of municipalities, forest properties and stands, local inventories are carried out periodically to inform forest management, in which airborne laser scanner (ALS) data are often used to estimate forest attributes. As local forest inventories are repeated, the availability of bitemporal field and ALS data is increasing. The aim of this study was to assess the utility of bitemporal ALS data for classification of dominant height change, aboveground biomass change, forest disturbances, and forestry activities. We used data obtained from 558 field plots and four repeated ALS-based forest inventories in southeastern Norway, with temporal resolutions ranging from 11 to 15 years. We applied the k-nearest neighbor method for classification of: (i) increasing versus decreasing dominant height, (ii) increasing versus decreasing aboveground biomass, (iii) undisturbed versus disturbed forest, and (iv) forestry activities, namely untouched, partial harvest, and clearcut. Leave-one-out cross-validation revealed overall accuracies of 96%, 95%, 89%, and 88% across districts for the four change classifications, respectively. Thus, our results demonstrate that various changes in forest structure can be classified with high accuracy at plot level using data from repeated ALS-based forest inventories.

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Section: Introductionmentioning

confidence: 99%

Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data

et al. 2019

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“…It is also associated with decreasing precipitation in beech sites due to going southward, negatively affecting the NDVI (r = 0.76 between the NDVI and LAT, Figures 3d and 5) [74,75]. On the contrary, some other researchers found positive correlations between the NDVI and site productivity [21,22,[76][77][78].…”

Section: Relationships Between the Stand Dominant Height (Sdh) And Sp...mentioning

confidence: 87%

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

Yener,

Guvendi

2023

Sustainability

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The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.

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“…Thus, it can be used as an indicator for these three factors, as shown in our analysis. We did not use a remote sensing-based version of the index, but it can be estimated with relatively high accuracy using bitemporal lidar data [74].…”

Section: Discussionmentioning

confidence: 99%

Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover

et al. 2022

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Remote sensing data comprise a valuable information source for many ecological landscape studies that may be under-utilized because of an overwhelming amount of processing methods and derived variables. These complexities, combined with a scarcity of quality control studies, make the selection of appropriate remote sensed variables challenging. Quality control studies are necessary to evaluate the predictive power of remote sensing data and also to develop parsimonious models underpinned by functional variables, i.e., cause rather than solely correlation. Cause-based models yield superior model transferability across different landscapes and ecological settings. We propose two basic guidelines for conducting such quality control studies that increase transferability and predictive power. The first is to favor predictors that are causally related to the response. The second is to include additional variables controlling variation in the property of interest and testing for optimum processing method and/or scale. Here, we evaluated these principles in predicting ground vegetation cover, soil moisture and pH under challenging conditions with forest canopies hindering direct remote sensing of the ground. Our model using lidar data combined with natural resource maps explained most of the observed variation in soil pH and moisture, and somewhat less variation of ground vegetation cover. Soil pH was best predicted by topographic position, sediment type and site index (R2 = 0.90). Soil moisture was best predicted by topographic position, radiation load, sediment type and site index (R2 = 0.83). The best model for predicting ground vegetation cover was a combination of lidar-based estimates for light availability below canopy and forest type, including an interaction between these two variables (R2 = 0.65).

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Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

Cited by 11 publications

References 52 publications

Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data

Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover

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