Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information

Irene Chrysafis, Georgios Korakis, Apostolos P. Kyriazopoulos and Giorgos Mallinis
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Irene Chrysafis: Forest Remote Sensing and Geospatial Analysis Laboratory, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, GR 68200 Orestiada, Greece
Georgios Korakis: Laboratory of Forest Botany, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, GR 68200 Orestiada, Greece
Apostolos P. Kyriazopoulos: Laboratory of Range Science, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, GR 68200 Orestiada, Greece
Giorgos Mallinis: Forest Remote Sensing and Geospatial Analysis Laboratory, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, GR 68200 Orestiada, Greece

Sustainability, 2020, vol. 12, issue 21, 1-14

Abstract: Measuring and monitoring tree diversity is a prerequisite for altering biodiversity loss and the sustainable management of forest ecosystems. High temporal satellite remote sensing, recording difference in species phenology, can facilitate the extraction of timely, standardized and reliable information on tree diversity, complementing or replacing traditional field measurements. In this study, we used multispectral and multi-seasonal remotely sensed data from the Sentinel-2 satellite sensor along with geodiversity data for estimating local tree diversity in a Mediterranean forest area. One hundred plots were selected for in situ inventory of tree species and measurement of tree diversity using the Simpson’s ( D 1 ) and Shannon ( H′ ) diversity indices. Four Sentinel-2 scenes and geodiversity variables, including elevation, aspect, moisture, and basement rock type, were exploited through a random forest regression algorithm for predicting the two diversity indices. The multi-seasonal models presented the highest accuracy for both indices with an R 2 up to 0.37. In regard to the single season, spectral-only models, mid-summer and mid-autumn model also demonstrated satisfactory accuracy (max R 2 = 0.28). On the other hand, the accuracy of the spectral-only early-spring and early-autumn models was significant lower (max R 2 = 0.16), although it was improved with the use of geodiversity information (max R 2 = 0.25).

Keywords: biodiversity; earth observation; multispectral; Mediterranean forest habitats; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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