Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction

Abstract

Forests contribute significantly to terrestrial biodiversity conservation. Monitoring of tree species diversity is vital due to climate change factors. Remote sensing imagery is a means of data collection for predicting diversity of tree species. Since various sensors have different spectral and spatial resolutions, it is worth comparing them to ascertain which could influence the accuracy of prediction of tree species diversity. Hence, this study evaluated the influence of the spectral and spatial resolutions of PlanetScope, RapidEye, Sentinel 2 and Landsat 8 images in diversity prediction based on the Shannon diversity index (H′), Simpson diversity Index (D1) and Species richness (S). The Random Forest regression was applied for the prediction using the spectral bands of the sensors as variables. The Sentinel 2 was the best image, producing the highest coefficient of determination (R2) under both the Shannon Index (R2 = 0.926) and the Species richness (R2 = 0.923). Both the Sentinel and RapidEye produced comparable higher accuracy for the Simpson Index (R2 = 0.917 and R2 = 0.915, respectively). The PlanetScope was the second-accurate for the Species richness (R2 = 0.90), whiles the Landsat 8 was the least accurate for the three diversity indices. The outcomes of this study suggest that both the spectral and spatial resolutions influence prediction accuracies of satellite imagery.

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

“…Although our results showed a correlation between species diversity and the red and blue bands, this correlation was positive, similar to Conti et al [61]. The positive correlation between the diversity indices (S and H ′ ) and spectral reflectance of bands 6 and 2 of the PlanetScope data confirms their sensitivity to the nature and structure of the forest and the vegetation properties (Gyamfi-Ampadu) [62]. According to Kulawardhana.…”

“…Our findings are consistent with those of Gyamfi-Ampadu. [62] and Imran et al [63] who demonstrated that the red part of the spectrum, that is, the red band, was best for estimating species diversity, hence their correlation with the species diversity (S and H ′ ). It must also be noted that the reserve lies in Ghana's tropical moist, semi-deciduous south-east forest zone and hence receives higher annual rainfall and a short dry season.…”

Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning

“…These presumptions are frequently challenging to prove (Dong et al, 2018;Cai et al, 2019). In the last decade, new satellites with higher spectral, spatial, and temporal resolution in combination with machine learning (ML) algorithms have given new insights into mapping TSC (Gyamfi-Ampadu et al, 2021, Waser et al, 2021.…”

Effect of Bit Depth on Cloud Segmentation of Remote-Sensing Images

“…It can be observed from the existing literature that there has been limited focus on the impact of bit depth on the information extraction process in remote-sensing images. In contrast, the influence of spatial [33,34] and spectral [35][36][37] resolution on remote-sensing data has been extensively studied in regards to classification accuracy and informationextraction capabilities. The results of cloud segmentation through remote sensing would be influenced by the bit depth of remote-sensing images [38].…”