Automatic Processing of Sentinel-2 Data for Monitoring Biodiversity in a User-Defined Area: An Example from Mount Kilimanjaro National Park

2021 ◽  
pp. 31-45
Author(s):  
Fortunata Msoffe ◽  
Dirk Zeuss
Keyword(s):  
National Park ◽  
Automatic Processing ◽  
Mount Kilimanjaro ◽  
Sentinel 2

scholarly journals ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 145-151
Author(s):  
M. T. Melis ◽  
F. Dessì ◽  
P. Loddo ◽  
A. Maccioni ◽  
M. Gallo ◽  
...  
Keyword(s):  
Land Cover ◽  
High Altitude ◽  
National Park ◽  
Brown Bear ◽  
Management Plan ◽  
Environmental Data ◽  
Mountain Ranges ◽  
Land Cover Map ◽  
Gilgit Baltistan ◽  
Sentinel 2

Abstract. Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes.Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing.

scholarly journals Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

2020 ◽  
Vol 12 (22) ◽  
pp. 3834 ◽  
Author(s):  
Junshi Xia ◽  
Naoto Yokoya ◽  
Tien Dat Pham
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Tree Species ◽  
National Park ◽  
Mangrove Species ◽  
Mangrove Tree ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Mangrove Communities ◽  
Sentinel 2

Mangrove forests play an important role in maintaining water quality, mitigating climate change impacts, and providing a wide range of ecosystem services. Effective identification of mangrove species using remote-sensing images remains a challenge. The combinations of multi-source remote-sensing datasets (with different spectral/spatial resolution) are beneficial to the improvement of mangrove tree species discrimination. In this paper, various combinations of remote-sensing datasets including Sentinel-1 dual-polarimetric synthetic aperture radar (SAR), Sentinel-2 multispectral, and Gaofen-3 full-polarimetric SAR data were used to classify the mangrove communities in Xuan Thuy National Park, Vietnam. The mixture of mangrove communities consisting of small and shrub mangrove patches is generally difficult to separate using low/medium spatial resolution. To alleviate this problem, we propose to use label distribution learning (LDL) to provide the probabilistic mapping of tree species, including Sonneratia caseolaris (SC), Kandelia obovata (KO), Aegiceras corniculatum (AC), Rhizophora stylosa (RS), and Avicennia marina (AM). The experimental results show that the best classification performance was achieved by an integration of Sentinel-2 and Gaofen-3 datasets, demonstrating that full-polarimetric Gaofen-3 data is superior to the dual-polarimetric Sentinel-1 data for mapping mangrove tree species in the tropics.

scholarly journals Cloud‐native Seascape Mapping of Mozambique’s Quirimbas National Park with Sentinel‐2

2020 ◽  
Author(s):  
Dimitris Poursanidis ◽  
Dimosthenis Traganos ◽  
Luisa Teixeira ◽  
Aurélie Shapiro ◽  
Lara Muaves
Keyword(s):  
National Park ◽  
Sentinel 2

scholarly journals A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 396 ◽  
Author(s):  
Premysl Stych ◽  
Barbora Jerabkova ◽  
Josef Lastovicka ◽  
Martin Riedl ◽  
Daniel Paluba
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bark Beetle ◽  
National Park ◽  
Support Vector ◽  
Landsat 8 ◽  
Kappa Index ◽  
Sentinel 2

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.

scholarly journals Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

2020 ◽  
Vol 12 (12) ◽  
pp. 1914 ◽  
Author(s):  
Josef Lastovicka ◽  
Pavel Svec ◽  
Daniel Paluba ◽  
Natalia Kobliuk ◽  
Jan Svoboda ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Satellite Data ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Forest Vegetation ◽  
Forest Recovery ◽  
Vegetation Changes ◽  
Tasseled Cap ◽  
Sentinel 2

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

scholarly journals The Impact of Groundwater Variability on Mangrove Greenness in Karimunjawa National Park based on Remote Sensing Study

2021 ◽  
Vol 925 (1) ◽  
pp. 012064
Author(s):  
J Prihantono ◽  
N S Adi ◽  
T Nakamura ◽  
K Nadaoka
Keyword(s):  
Soil Moisture ◽  
Water Table ◽  
National Park ◽  
Correlation Coefficients ◽  
Rainfall Data ◽  
Strong Positive Correlation ◽  
Positive Correlation ◽  
Monthly Average ◽  
The Impact ◽  
Sentinel 2

Abstract This study aims to understand the impact of groundwater table on soil moisture and mangrove greenness in different seasons in Karimunjawa National Park (KNP). We used Sentinel-2 L2A satellite imagery, Global Precipitation Measurement (GPM) satellite rainfall data, and water table observations at KNP. This study estimates Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on time series Sentinel-2 imagery in 2019-2020 using Google Earth Engine. In addition, we compared the monthly average rainfall data, the monthly average water table data, and the monthly average NDVI, NDWI data extracted at the water table observation points. NDVI is a method to estimate mangrove greenness, and NDWI to estimate soil moisture. The obtained results indicate that NDVI and NDWI in the near shoreline area show a higher value than in the middle area of the KNP that is far from the shoreline. In addition, the value of the NDVI and NDWI correlation coefficients is 0.94, which indicates a positive and strong correlation. Moreover, The NDWI and water table correlation coefficients are 0.79, which indicates a relatively strong positive correlation. Furthermore, the correlation between rainfall and the water table is 0.61, which indicates a relatively strong positive correlation. Thus, these findings show that the water table influences soil moisture and then affects the mangrove greenness. Besides that, the water table change is governed by rainfall, and therefore, the mangrove greenness in KNP depends on seasons and is vulnerable to drought.

scholarly journals Vegetation cover dynamics mapping of the Curonian Spit National Park usingsatellite imagery Alos and Sentinel-2

2019 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
A.D. Nikitina ◽  
◽  
S.V. Knyazeva ◽  
E.A. Gavrilyuk ◽  
E.V. Tikhonova ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
National Park ◽  
Curonian Spit ◽  
Sentinel 2
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