scholarly journals A hybrid approach of remote sensing for mapping vegetation biodiversity in a tropical rainforest

2020 ◽  
Vol 21 (9) ◽  
Author(s):  
Wahyu Wardhana ◽  
EMMA SORAYA ◽  
DJOKO SOEPRIJADI ◽  
BEKTI LARASATI ◽  
YAASIIN HENDRAWAN TRI HUTOMO ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Tropical Rainforest ◽  
Hybrid Approach ◽  
Remote Sensing Data ◽  
Hierarchical Cluster ◽  
Vegetation Density ◽  
Resolution Image ◽  
Medium Resolution ◽  
Long Time

Abstract. Wardhana W, Widyatmanti W, Soraya E, Soeprijadi D, Larasati B, Umarhadi DA, Hutomo YHT, Idris F, Wirabuana PYAP. 2020. A hybrid approach of remote sensing for mapping vegetation biodiversity in a tropical rainforest. Biodiversitas 21: 3946-3953.  Vegetation biodiversity is one of the most important indicators to evaluate the sustainability of tropical rainforest. It is commonly described by three essential variables, i.e. richness, heterogeneity, and evenness. That information is frequently collected from periodic forest inventory using terrestrial method. However, this effort needs a long-time consuming, high cost, and almost impossible to implement in the area of tropical rainforest with hard accessibility. This study investigates the potential of remote sensing as an alternative method for mapping vegetation biodiversity in a tropical rainforest. A hybrid approach of remote sensing using medium and high-resolution images was developed to recognize the attributes of vegetation biodiversity by considering three parameters derived from remote sensing data, including canopy density (C), crown diameter (D), and tree density (N). The use of a medium resolution image aimed to categorize vegetation density using Modified Soil-Adjusted Value Index (MSAVI) while a high-resolution image was utilized to acquire a more detailed spectrum for determining C, D, and N in every class of vegetation density. The relationship between C, D, N, and richness, heterogeneity, evenness was explained using hierarchical cluster analysis. Our study discovered the attributes of vegetation biodiversity in a tropical rainforest could be potentially recognized by combining C, D, and N as predictor variables.

scholarly journals USING MEDIUM RESOLUTION SATELLITE REMOTE SENSING DATA FOR COASTLINE DETECTION AND INTERPRETATION

2020 ◽  
Vol 2 (5) ◽  
pp. 56-62
Author(s):  
E.I. Volynets ◽  
◽  
A.V. Volynetc ◽  
E.A. Panidi ◽  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Resolution Image ◽  
Position Accuracy ◽  
Medium Resolution ◽  
Water Indices

Remote sensing data are widely used in coastal zones monitoring, since they provide radiometric information with the possibility to automate the data processing. Due to the lack of high resolution satellite images in free access using the medium resolution satellite images is widespread. The study is dedicated to the development of a coastline detection method based on medium resolution satellite images. It is proposed to use a semi-automatic method based on uncontrolled classification of a mid-resolution image by water indices, followed by expert refinement of classes and the use of machine learning methods. The shorelines of the eastern part of the Gulf of Finland have been extracted from Landsat 8 Level-2 image, using proposed method. The position accuracy of the generated shorelines has been analyzed using manually digitized shoreline from high-resolution image Resurs-P1 processing level 2А. The results showed that in the test areas the best output for extracting the coastline are given by the MNDWI with a fixed threshold value equal to zero. The Random Forest machine learning algorithm was used to refine the type of coastline, especially in wetlands where water indices showed poor accuracy. The study showed a significant increase in the position accuracy with the use of the algorithm. However, the accuracy of manually classified wetlands for training the model has a significant impact on the result.

Evaluating high-resolution remote sensing data for reconstructing the recent evolution of supra glacial debris

2018 ◽  
Vol 42 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Roberto S Azzoni ◽  
Davide Fugazza ◽  
Andrea Zerboni ◽  
Antonella Senese ◽  
Carlo D’Agata ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
European Alps ◽  
Pixel Size ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Debris Cover ◽  
Resolution Data ◽  
Sentinel 2

Over the last decades, the expansion of supraglacial debris on worldwide mountain glaciers has been reported. Nevertheless, works dealing with the detection and mapping of supraglacial debris and detailed analyses aimed at identifying the temporal and spatial trends affecting glacier debris cover are still limited. In this study, we used different remote sensing sources to detect and map the supraglacial debris cover, to analyze its evolution, and to assess the potential of different remote-sensed image data. We performed our analyses on the glaciers of Ortles-Cevedale Group (Stelvio Park, Italy), one of the most representative glacierized sectors of the European Alps. High-resolution airborne orthophotos (pixel size 0.5 m × 0.5 m) acquired during the summer season in the years 2003, 2007, and 2012 permitted to map in detail, with an error lower than ±5%, the supraglacial debris cover through a maximum likelihood classification. Our findings suggest that over the period 2003–2012, supraglacial debris cover increased from 16.7% to 30.1% of the total glacier area. On Forni Glacier we extended these quantification thanks to the availability of UAV (Unmanned Aerial Vehicle) orthophotos from 2014 and 2015 (pixel size 0.15 m × 0.15 m): this detailed analysis permitted to confirm debris is increasing on the glacier melting surface (+20.4%) and confirms the requirement of high-resolution data in debris mapping on Alpine glaciers. Finally, we also checked the suitability of medium-resolution Landsat ETM+ data and Sentinel 2 data to map debris in a typical Alpine glaciation scenario where small ice bodies (<0.5 km2) are the majority. The results we obtained suggest that medium-resolution data are not suitable for a detailed description and evaluation of supraglacial debris cover in the Alpine scenario, nevertheless Sentinel 2 proved to be appropriate for a preliminary mapping of the main debris features.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

scholarly journals A Computational Methodology for the Calibration of Tephra Transport Nowcasting at Sakurajima Volcano, Japan

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 104
Author(s):  
Alexandros P. Poulidis ◽  
Atsushi Shimizu ◽  
Haruhisa Nakamichi ◽  
Masato Iguchi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Volcanic Eruptions ◽  
Weather Research And Forecasting ◽  
Sakurajima Volcano ◽  
Observation Network ◽  
Complementary Strategy ◽  
Physical Quantities ◽  
Do So

Ground-based remote sensing equipment have the potential to be used for the nowcasting of the tephra hazard from volcanic eruptions. To do so raw data from the equipment first need to be accurately transformed to tephra-related physical quantities. In order to establish these relations for Sakurajima volcano, Japan, we propose a methodology based on high-resolution simulations. An eruption that occurred at Sakurajima on 16 July 2018 is used as the basis of a pilot study. The westwards dispersal of the tephra cloud was ideal for the observation network that has been installed near the volcano. In total, the plume and subsequent tephra cloud were recorded by 2 XMP radars, 1 lidar and 3 optical disdrometers, providing insight on all phases of the eruption, from plume generation to tephra transport away from the volcano. The Weather Research and Forecasting (WRF) and FALL3D models were used to reconstruct the transport and deposition patterns. Simulated airborne tephra concentration and accumulated load were linked, respectively, to lidar backscatter intensity and radar reflectivity. Overall, results highlight the possibility of using such a high-resolution modelling-based methodology as a reliable complementary strategy to common approaches for retrieving tephra-related quantities from remote sensing data.

scholarly journals Capacity Development for Use of Remote Sensing for REDD+ MRV Using Online and Offline Activities: Impacts and Lessons Learned

2021 ◽  
Vol 13 (11) ◽  
pp. 2172
Author(s):  
Sarah Carter ◽  
Martin Herold ◽  
Inge Jonckheere ◽  
Andres Espejo ◽  
Carly Green ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Hybrid Approach ◽  
Remote Sensing Data ◽  
Lessons Learned ◽  
Multiple Partners ◽  
Large Audience ◽  
Forest Cover Changes ◽  
The Impact ◽  
And Training

Four workshops and a webinar series were organized, with the aim of building capacity in countries to use Earth Observation Remote Sensing data to monitor forest cover changes and measure emissions reductions for REDD+ results-based payments. Webinars and workshops covered a variety of relevant tools and methods. The initiative was collaboratively organised by a number of Global Forest Observations Initiative (GFOI) partner institutions with funding from the World Bank’s Forest Carbon Partnership Facility (FCPF). The collaborative approach with multiple partners proved to be efficient and was able to reach a large audience, particularly in the case of the webinars. However, the impact in terms of use of tools and training of others after the events was higher for the workshops. In addition, engagement with experts was higher from workshop participants. In terms of efficiency, webinars are significantly cheaper to organize. A hybrid approach might be considered for future initiatives; and, this study of the effectiveness of both in-person and online capacity building can guide the development of future initiatives, something that is particularly pertinent in a COVID-19 era.

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