scholarly journals Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study

2011 ◽  
Vol 3 (9) ◽  
pp. 1805-1816 ◽  
Author(s):  
Susan K. Maxwell
Keyword(s):  
Satellite Imagery ◽  
Pesticide Use ◽  
Field Level ◽  
Crop Field ◽  
Landsat Satellite

PESTICIDE USE AND RISK PERCEPTIONS FOR HUMAN HEALTH AND THE ENVIRONMENT: A CASE STUDY OF ALGERIAN FARMERS

2020 ◽  
Vol 76 (5) ◽  
Author(s):  
Soudani Nafissa ◽  
Belhamra Mohammed ◽  
Toumi Khaoula
Keyword(s):  
Human Health ◽  
Risk Perceptions ◽  
Pesticide Use

scholarly journals Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 637
Author(s):  
Huong Thi Thuy Nguyen ◽  
Giles E. S. Hardy ◽  
Tuat Van Le ◽  
Huy Quoc Nguyen ◽  
Hoang Huy Nguyen ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Monsoon Season ◽  
Coastal Development ◽  
Northern Coast ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Mangrove Restoration ◽  
Coast Of Vietnam ◽  
Landsat Satellite

Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

Reconstruction of sand wave bathymetry using both satellite imagery and multi-beam bathymetric data: a case study of the Taiwan Banks

2014 ◽  
Vol 35 (9) ◽  
pp. 3286-3299 ◽  
Author(s):  
Xiekai He ◽  
Ninghua Chen ◽  
Huaguo Zhang ◽  
Bin Fu ◽  
Xiaozhen Wang
Keyword(s):  
Satellite Imagery ◽  
Sand Wave ◽  
Bathymetric Data

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Mapping and Monitoring Erosion-Accretion in an Alluvial River Using Satellite Imagery – The River Bank Changes of the Padma River in Bangladesh

2018 ◽  
Vol 37 (3) ◽  
pp. 87-95 ◽  
Author(s):  
Mohammad Maruf Billah
Keyword(s):  
Satellite Imagery ◽  
Bank Erosion ◽  
River Bank ◽  
River Bank Erosion ◽  
Island Area ◽  
River Bed ◽  
Drastic Increase ◽  
Padma River ◽  
Landsat Satellite ◽  
The Right

Abstract The Padma river is widely known for its dynamic and disastrous behaviour, and the river has been experiencing intense and frequent bank erosion and deposition leading to the changes and shifting of bank line. In this paper, a time series of Landsat satellite imagery MSS, TM and OLI and TIRS images and are used to detect river bank erosion-accretion and bank line shifting during the study period 1975–2015. This study exhibits a drastic increase of erosion and accretion of land along the Padma river. The results show that from 1975 to 2015, the total amount of river bank erosion is 49,951 ha of land, at a rate of 1,249 ha a−1 and the total amount of accretion is 83,333 ha of land, at a rate of 2,083 ha a−1. Throughout the monitoring period, erosion-accretion was more pronounced in the right part of the river and bank line had been shifting towards the southern direction. The paper also reveals that the total area of islands had been increased significantly, in 2015 there was about 50,967 ha of island area increased from 20,533 ha of island area in 1975, and the results evidence consistency of sedimentation in the river bed.

Sign in / Sign up

Export Citation Format

Share Document