scholarly journals Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada

2011 ◽  
Vol 3 (5) ◽  
pp. 975-1005 ◽  
Author(s):  
Jennifer D. O’Neill ◽  
Maycira Costa ◽  
Tara Sharma
Keyword(s):  
Remote Sensing ◽  
Zostera Marina ◽  
National Park ◽  
Gulf Islands ◽  
In Situ Spectra

scholarly journals Coral Reef Zone Analysis in Development of Segmentation Ecotourism in Karimunjawa National Park

2019 ◽  
pp. 1-15
Author(s):  
Aramita Livia Ardis ◽  
Mega Laksmini Syamsudin ◽  
Herman Hamdani ◽  
Lantun Paradhita Dewanti
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Carrying Capacity ◽  
National Park ◽  
Coral Cover ◽  
Sea Surface ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Coral Reef Ecosystems

Karimunjawa is one of the main destinations that present underwater beauty that is quite popular. But due to increased tourism activities provide economic benefits but also have a negative impact on coral reef ecosystems so that prudent and sustainable management is needed, these characteristics are felt capable of being helped by remote sensing technology. The purpose of this research is to analyze the coral reef zoning for the development of ecotourism segmentation and the carrying capacity of coral reef ecosystems and to map the condition of coral reef ecosystems in the Karimunjawa National Park area through remote sensing technology. The method used in data collection uses a survey method which is divided into 2 types in-situ conducted on 19th April 2019 to 2nd May 2019 and ex-situ taken for 4 years for coral cover and 1 year for sea surface temperature. By using quantitative descriptive analysis, land suitability results are obtained based on the land suitability index approach and the percentage of coral cover in determining the mapping of ecotourism segmentation areas. The results of this research show that through in-situ approach, data collection in three stations on Sintok and Menjangan Kecil Islands has good coral cover while Cemara Besar is damaged. The appropriate Tourism Conformity Index value is on Menjangan Kecil Island while the other two stations are not so that the carrying capacity calculation is only done on the appropriate and very appropriate island. Inversely proportional through the analysis of the Scenic Beauty Estimation value, Cemara Besar Island which shows a high value while on the Menjangan Kecil Island the lowest. Spatial analysis shows that the fluctuation in sea surface temperature during one year is not too significant and is still limited to the optimum temperature range for coral growth so that it does not affect the conditions causing damage to coral reefs, called bleaching. Looking at the distribution of coral reefs via satellite, over the past 4 years shows an increase in dead coral cover leaving 6,752,802 m2 in 2019.

THE ESSENTIAL CONTRIBUTION OF CAPTIVE SUMATRAN ELEPHANT IN ELEPHANT TRAINING CENTER, WAY KAMBAS NATIONAL PARK FOR WILDLIFE GENETICS CONSERVATION

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Elly Lestari Rustiati ◽  
Priyambodo Priyambodo ◽  
Yanti Yulianti ◽  
Eko Agus Srihanto ◽  
Dian Neli Pratiwi ◽  
...  
Keyword(s):  
National Park ◽  
In Situ Conservation ◽  
Training Center ◽  
Conservation Effort ◽  
In The Wild ◽  
Wildlife Genetics ◽  
Way Kambas National Park ◽  
Human Conflict ◽  
Essential Contribution

Way Kambas National Park (WKNP) is home of five protected big mammals including sumatran elephants.  It shares its border with 22 of 37 villages surrounding the national park.  Understanding their existence in the wild is a priority, and  wildlife genetics is a crucially needed. Besides poaching and habitat fragmentation, wildlife-human conflict is one big issue.  Elephant Training Center (ETC) in WKNP is built for semi in-situ conservation effort on captive sumatran elephants that mainly have conflict histories with local people.  Participative observation and bio-molecular analysis were conducted to learn the importance of captive Sumatran elephant for conservation effort.  Through captive sumatran elephants, database and applicable methods are expected to be developed supporting the conservation of their population in the wild.  Participative observation and molecular identification was carried on captive sumatran elephants in ETC, WKNP under multiple year Terapan grant of Ministry of Research and Technology Higher Education, Indonesia. Gene sequence and cytological analyses showed that the captive sumatran elephants are closely related and tend to be domesticated.  Translocation among ETC to avoid inbreeding, and maintaining the captive sumatran elephant as natural as possible are highly recommended. Developing genetic database can be a reference for both captive and wild sumatran elephants.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data

2021 ◽  
pp. 105623
Author(s):  
Stefan Becker ◽  
Ramesh Prasad Sapkota ◽  
Binod Pokharel ◽  
Loknath Adhikari ◽  
Rudra Prasad Pokhrel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Reanalysis Data ◽  
In Situ Measurements
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