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

2020 ◽  
Vol 12 (21) ◽  
pp. 9250
Author(s):  
Irene Chrysafis ◽  
Georgios Korakis ◽  
Apostolos P. Kyriazopoulos ◽  
Giorgos Mallinis
Keyword(s):  
Tree Species ◽  
Field Measurements ◽  
Biodiversity Loss ◽  
Diversity Indices ◽  
Early Spring ◽  
Tree Diversity ◽  
Remotely Sensed Data ◽  
Random Forest Regression ◽  
Sentinel 2

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 (D1) 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 R2 up to 0.37. In regard to the single season, spectral-only models, mid-summer and mid-autumn model also demonstrated satisfactory accuracy (max R2 = 0.28). On the other hand, the accuracy of the spectral-only early-spring and early-autumn models was significant lower (max R2 = 0.16), although it was improved with the use of geodiversity information (max R2 = 0.25).

scholarly journals Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Fardin Moradi ◽  
Ali Asghar Darvishsefat ◽  
Manizheh Rajab Pourrahmati ◽  
Azade Deljouei ◽  
Stelian Alexandru Borz
Keyword(s):  
Aboveground Biomass ◽  
Nearest Neighbor ◽  
Vegetation Indices ◽  
Field Measurements ◽  
K Nearest Neighbor ◽  
Remotely Sensed Data ◽  
Northern Iran ◽  
Hyrcanian Forests ◽  
Sentinel 2

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

scholarly journals Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

scholarly journals Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Silva Fennica ◽  
2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Mikko Kukkonen ◽  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
Lauri Korhonen ◽  
Petteri Packalen
Keyword(s):  
Tree Species ◽  
Small Area ◽  
Laser Scanning ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Remotely Sensed Data ◽  
Alternative Source ◽  
Species Specific ◽  
Species Proportions ◽  
Sentinel 2

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

scholarly journals Ecological exploration of Kuwana forest: A tropical moist deciduous forest of eastern Terai, India

2017 ◽  
Vol 6 (12) ◽  
pp. 1811 ◽  
Author(s):  
Omesh Bajpai ◽  
Shraddha Suman ◽  
Nirmala Upadhyay
Keyword(s):  
Tree Species ◽  
Deciduous Forest ◽  
Uttar Pradesh ◽  
Principal Component ◽  
Diversity Indices ◽  
Tree Diversity ◽  
Forest Community ◽  
Importance Value Index ◽  
Importance Value ◽  
Lowland Forest

The present study was conducted in the Kuwana forest of Gonda forest division in Uttar Pradesh to explore its ecological inventories. Random stratified sampling was adopted to collect the basic information like frequency, density and abundance for the calculation of importance value index (IVI). On the basis of principal component analysis (PCA) plot, three forest communities were identified and named as, Syzygium Lowland Forest (SLF), Shorea Miscellaneous Forest (SMF) and Mallotus Miscellaneous Forest (MMF). MMF community allowed the maximum 39 while SLF minimum 18 tree species growing in it. Conversely, SMF community showed higher heterogeneous tree diversity validated by lower Dominance index (0.088) and higher Simpson index (0.912). The values of these two indices were found very low in comparison with their range for tropical forests of India. On the otherhand the diversity indices (Shannon & Fisher alpha) was calculated as maximum (2.797 & 11.960 respectively) for MMF community, which indicates the existence of better tree diversity in this forest community. The higher values of Evenness & Equitability indices (0.646 & 0.859 respectively) for SMF community showed the more evenly distribution of tree species in this community.

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

2021 ◽  
Vol 13 (5) ◽  
pp. 1033
Author(s):  
Enoch Gyamfi-Ampadu ◽  
Michael Gebreslasie ◽  
Alma Mendoza-Ponce
Keyword(s):  
Species Richness ◽  
Species Diversity ◽  
Tree Species ◽  
Diversity Index ◽  
Diversity Indices ◽  
Shannon Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Tree Species Diversity ◽  
Sentinel 2

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.

scholarly journals Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

2018 ◽  
Vol 10 (11) ◽  
pp. 1687 ◽  
Author(s):  
Joan-Cristian Padró ◽  
Francisco-Javier Muñoz ◽  
Luis Ávila ◽  
Lluís Pesquer ◽  
Xavier Pons
Keyword(s):  
Near Infrared ◽  
Field Measurements ◽  
Systematic Sampling ◽  
Landsat 8 ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Central Hypothesis ◽  
Sentinel 2A ◽  
Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

Climate resilience through natural regeneration in degraded natural forests of south-eastern hilly region of Bangladesh

2019 ◽  
Vol 48 (3) ◽  
pp. 417-425
Author(s):  
Md Khayrul Alam Bhuiyan ◽  
Md Akhter Hossain ◽  
Abdul Kadir Ibne Kamal ◽  
Mohammed Kamal Hossain ◽  
Mohammed Jashimuddin ◽  
...  
Keyword(s):  
Tree Species ◽  
Natural Regeneration ◽  
Diversity Indices ◽  
Acacia Auriculiformis ◽  
Quantitative Structure ◽  
Natural Forests ◽  
Climate Resilience ◽  
Hilly Region ◽  
Major Cluster ◽  
Ecological Dominance

A study was conducted by using 5m × 5m sized 179 quadrates following multistage random sampling method for comparative regenerating tree species, quantitative structure, diversity, similarity and climate resilience in the degraded natural forests and plantations of Cox's Bazar North and South Forest Divisions. A total of 70 regenerating tree species were recorded representing maximum (47 species) from degraded natural forests followed by 43 species from 0.5 year 39 species from 1.5 year and 29 species from 2.5 year old plantations. Quantitative structure relating to ecological dominance indicated dominance of Acacia auriculiformis, Grewia nervosa and Lithocarpus elegans seedlings in the plantations whereas seedlings of Aporosa wallichii, Suregada multiflora and Grewia nervosa in degraded natural forests. The degraded natural forests possess higher natural regeneration potential as showed by different diversity indices. The dominance-based cluster analysis showed 2 major cluster of species under one of which multiple sub-clusters of species exists. Poor plant diversity and presence of regenerating exotic species in the plantations indicated poor climate resilience of forest ecosystem in terms of natural regeneration.

Tree Diversity Conservation Initiatives in Sacred Groves of Kathmandu Valley, Nepal

2020 ◽  
Vol 19 (1) ◽  
pp. 60-68
Author(s):  
Laxmi Joshi Shrestha ◽  
Mohan Devkota ◽  
Bhuvan Keshar Sharma
Keyword(s):  
Tree Species ◽  
Management System ◽  
Survey Methods ◽  
Belief Systems ◽  
Decisive Role ◽  
Diversity Indices ◽  
Kathmandu Valley ◽  
Sacred Grove ◽  
Sacred Groves ◽  
The Government

 The study was conducted in two sacred groves of Kathmandu Valley, Pashupati Sacred Grove, and Bajrabarahi Sacred Grove, aiming to analyze the diversity of tree species and their role in conserving biodiversity. Parallel transects with concentric circular plot survey methods were applied for data collection. During the study, 23 tree species belonging to 22 genera and 15 families were recorded in Pashupati sacred grove, whereas only 19 tree species belonging to 16 genera and 13 families were recorded from Bajrabarahi Sacred Grove. The Shannon-Weiner diversity indices were higher (H=1.91) in Pashupati Sacred Grove compared to Bajrabarahi Sacred Grove, with 1.80 Shanon-Weiner Indices. Three types of forest were recorded from Pashupati Sacred Grove, namely the Schima-Pyrus forest, Myrsine-Persea forest, and Quercus-Myrsine forest, and only one Neolitsiacuipala forest from Bajrabarahi Sacred Grove. The sacred grove is one of the pioneers and community-based management regimes of the forest resource management system. It plays a decisive role in biodiversity conservation as it associated with many taboos and belief systems, thus providing a better opportunity for conservation compared to that of the government management system.

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