scholarly journals Modelling and Mapping Above Ground Biomass Using Sentinel 2 and Planet Scope Remotely Sensed Data in West Usambara Tropical Rainforests, Tanzania

2021 ◽  
Author(s):  
Ernest William Mauya ◽  
Sami Madundo
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Tropical Rainforests ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Tropical Regions ◽  
Sentinel 2

Abstract Forest biomass estimation using field -based inventories at a large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, optical remote sensing signals had gained a wide acceptance for larger scale estimation of AGB at different spatial scales, ranging from local to global. However, their applicability in tropical forests is still limited. In this study, we investigated the performance of Sentinel 2 and Planet Scope remotely sensed data for AGB modelling, predicting and mapping in the tropical rainforest of Tanzania. A total of 296 field inventory plots were measured across the west Usambara mountain forests. AGB values were computed for each of the field plot in Mg/ha, and related with remotely sensed predictor variables using parametric and non- parametric statistical methods. Band values, vegetation indices and texture based variables were derived from each of the remotely sensed data. The AGB models were developed and validated using k-fold cross validation and their relative root mean square error (cvRMSEr%) were used to judge their accuracies. Relative efficiency (RE) of each dataset as compared to pure field inventory was also computed. The results showed that, Sentinel 2 based model fitted using generalized linear models (RMSEr = 67.00 % and pseudo-R2= 20%) had better performance as compared to Planet Scope based models (cvRMSEr = 72.1 % and pseudo-R2= 5.2%). Overall GLMs resulted into a models with less prediction error as compared to random forest when using Sentinel 2 data. However, for the Planet Scope, there was marginal improvement of using random forest (cvRMSEr = 72.0%) as compared to GLMs. Models, that in cooperated texture variables resulted into better prediction accuracy as compared to those with band values and indices only. The R.E values for Sentinel2 and Planet Scope were 1.2 and 1.1 respectively. Our study had demonstrated that Sentinel 2 and Planet Scope remotely sensed data can be used to develop cost-effective method for AGB estimation within the context of tropical rainforests of Tanzania. Further studies are however encouraged to look more on the best way of optimizing the efficiency of the two data sources in AGB estimations.

scholarly journals Application of Random Forest Approach to Biomass Estimation Using Remotely Sensed Data

2021 ◽  
Author(s):  
Nyamjargal Erdenebaatar ◽  
Batbileg Bayaraa ◽  
Amarsaikhan Damdinsuren
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Biomass Estimation ◽  
Remotely Sensed Data

scholarly journals Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Gáspár Albert ◽  
Seif Ammar
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Geological Mapping ◽  
Vegetation Coverage ◽  
Remotely Sensed Data ◽  
Random Forest Classification ◽  
Radar Images ◽  
Forest Classification ◽  
Geological Map ◽  
Sentinel 2

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

scholarly journals Spatial scales of temperature and salinity variability estimated from Argo observations

2015 ◽  
Vol 12 (4) ◽  
pp. 1793-1814
Author(s):  
F. Ninove ◽  
P. Y. Le Traon ◽  
E. Remy ◽  
S. Guinehut
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Deep Ocean ◽  
General Tendency ◽  
Zero Crossing ◽  
Tropical Regions ◽  
Covariance Functions ◽  
Salinity Variability ◽  
First Time

Abstract. Argo observations from 2005 to 2013 are used to characterize spatial scales temperature and salinity variations from the surface down to 1500 m. Simulations are first performed to analyze the sensitivity of results to Argo sampling; they show that several years of Argo observations are required to estimate the spatial scales of ocean variability over 20° × 20° boxes. Spatial scales are then computed over several large scale areas. Zonal and meridional spatial scales (Lx and Ly which are also zero crossing of covariance functions) vary as expected with latitudes. Scales are of about 100 km at high latitudes and more of 700 km in the Indian and Pacific equatorial/tropical regions. Zonal and meridional scales are similar: except in these tropical/equatorial regions where zonal scales are much larger (by a factor of 2 to 3) than meridional scales. Spatial scales are the largest close to the surface and have a general tendency for temperature to increase in deeper layers. There are significant differences between temperature and salinity scales, in particular, in the deep ocean. Results are consistent with previous studies based on sparse in-situ observations or satellite altimetry. They provide, however, for the first time a global description of temperature and salinity scales of variability and a characterization of their variations according to depths.

Modification of a groundwater prospect zone index using remotely sensed data and the analytic network process in the eastern part of Lesvos island, Greece.

2020 ◽  
Vol 11 (4) ◽  
pp. 126-143
Author(s):  
Terpsichori MITSI ◽  
◽  
Demetre ARGIALAS ◽  
Konstantinos VAMVOUKAKIS ◽  
◽  
...  
Keyword(s):  
Ground Water ◽  
Analytic Network Process ◽  
Remotely Sensed ◽  
Groundwater Potential ◽  
Landsat 8 ◽  
Groundwater Prospect ◽  
Remotely Sensed Data ◽  
Potential Index ◽  
Network Process ◽  
Sentinel 2

Because of climate change and overpopulation, the demand for water is increasing. Groundwater constitutes an alternative renewable source of aquifer, so the spatial distribution of ground water provides important information on its qualitative and quantitative status. This paper develops a methodology for delineating potential ground water zones using remotely sensed data and GIS. The developed methodology was based on the empirical index GPI (MGPI – Modified Groundwater Potential Index) and was applied to the eastern part of Lesvos Island, Greece. To evaluate the criteria used for the result, the Analytic Network Process (ANP) was applied to weight each parameter. The dataset used consists of satellite images derived from Sentinel 2 and Landsat 8, which were combined with vector and raster data, to create the necessary thematic layers. To validate the results, existing ground water zones from the Municipal Water Company of Lesvos were used.

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 Evaluation of Multi-Reanalysis Solar Radiation Products Using Global Surface Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Xiaomin Peng ◽  
Jiangfeng She ◽  
Shuhua Zhang ◽  
Junzhong Tan ◽  
Yang Li
Keyword(s):  
Solar Radiation ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Solar Radiation ◽  
Hydrological Process ◽  
China Meteorological Administration ◽  
Surface Solar Radiation ◽  
Climate Zones ◽  
Tropical Regions ◽  
Spatial Coverage

Solar radiation incident at the Earth’s surface is an essential driver of the energy exchange between the atmosphere and the surface and is also an important input variable in the research on the surface eco-hydrological process. The reanalysis solar radiation dataset is characterized by a long time series and wide spatial coverage and is used in the research of large-scale eco-hydrological processes. Due to certain errors in their production process of the reanalysis of solar radiation products, reanalysis products should be evaluated before application. In this study, three global solar-radiation reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) in different temporal scales and climate zones were evaluated using surface solar-radiation observations from the National Meteorological Information Center of the China Meteorological Administration (CMA, Beijing, China) and the Global Energy Balance Archive (GEBA, Zürich, Switzerland) from 2000 to 2009. All reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) overestimated with an annual bias of 14.86 W/m2, 22.61 W/m2, and 31.85 W/m2; monthly bias of 15.17 W/m2, 21.29 W/m2, and 36.91 W/m2; and seasonal bias of 15.08 W/m2, 21.21 W/m2, and 36.69 W/m2, respectively. In different Köppen climate zones, the annual solar radiation of ERA-Interim performed best in cold regions with a bias of 10.30 W/m2 and absolute relative error (ARE) of 8.98%. However, JRA-55 and NCEP-DOE showed the best performance in tropical regions with a bias of 20.08 W/m2 and −0.12 W/m2, and ARE of 11.00% and 9.68%, respectively. Overall, through the evaluations across different temporal and spatial scales, the rank of the three reanalysis products in order was the ERA-Interim, JRA-55, and NCEP-DOE. In addition, based on the evaluation, we analyzed the relationship between the error (ARE) of the reanalysis products and cloud cover, aerosol, and water vapor, which significantly influences solar radiation and we found that cloud was the main cause for errors in the three solar radiation reanalysis products. The above can provide a reference for the application and downscaling of the three solar radiation reanalysis products.

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