- Remote Sensing Applications to Monitoring Wetland Dynamics: A Case Study on Qinghai Lake Ramsar Site, China

2013 ◽  
pp. 504-519
Keyword(s):  
Remote Sensing ◽  
Qinghai Lake ◽  
Ramsar Site ◽  
Wetland Dynamics ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

scholarly journals Remote sensing in the water management practice.

1986 ◽  
Vol 34 (3) ◽  
pp. 317-328
Author(s):  
M. Menenti ◽  
G.J.A. Nieuwenhuis
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Management Practice ◽  
Model Calculations ◽  
Sensing Applications ◽  
Irrigated Area ◽  
Crop Type ◽  
Remote Sensing Applications

In this examination of the use of remote sensing in water management 3 case studies are presented. The first concerned water management in the eastern Netherlands and remote sensing was used to provide evapotranspiration values. The description of the hydrological conditions was markedly improved by combining remote sensing and hydrological model calculations. A case study in Argentina using Greenness Vegetation Index showed how remote sensing can be used to give data on irrigated area and crop type. In the third case study, remote sensing was used to investigate groundwater losses in a desert area in Libya. The use of theoretical and experimental research in remote sensing, remote sensing applications in the Netherlands and remote sensing applications in developing countries are discussed. (Abstract retrieved from CAB Abstracts by CABI’s permission)

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