Abstract
Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.
Highlights
Leaf area index (LAI; total one-sided area of leaf tissue per unit ground surface) is widely recognized as a key variable for a broad range of agro-environmental studies, given its tight relationships with primary production [1], transpiration [2], energy exchange [3], CO2 sequestration [4] and with a variety of other eco-physiological processes [5]
The digital hemispherical photography (DHP)—like other methods based on image processing (e.g., PocketLAI, [4])—can be classified in the second category, it derives gap fraction from image segmentation instead of using the above-to-below canopy luminance ratio
We quantified the trueness for DHP using artificial images with known canopy cover
Summary
Leaf area index (LAI; total one-sided area of leaf tissue per unit ground surface) is widely recognized as a key variable for a broad range of agro-environmental studies, given its tight relationships with primary production [1], transpiration [2], energy exchange [3], CO2 sequestration [4] and with a variety of other eco-physiological processes [5]. The direct measurement of LAI is often unfeasible, especially in operational context or when woody canopies are involved [6]. In these cases, LAI is usually estimated using indirect methods, based on the measurement of the amount of radiation (i) reflected/absorbed by the canopy (remote sensing techniques) or (ii) transmitted through it (optical proximal instruments). DHP is one of the less expensive techniques and gives to users the possibility to reprocess archives (e.g., correction of incorrect exposure, removal of unwanted objects) because of its permanent image recording For these reasons and for the partial suitability of other
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