Addressing Current Problems with Achieving Physical Consistency Across the Electromagnetic Spectrum Between Ice Crystal Models, Remote-Sensing, and Large-Scale Models

Traditional methods for measurement of vegetative biomass can be time-consuming and laborÂintensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of green vegetative biomass present in an area (Tucker and Sellers 1986).

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Management Options to Improve Water Quality in Lake Peipsi: Insights from Large Scale Models and Remote Sensing

Water Resources Management ◽

10.1007/s11269-018-2156-5 ◽

2018 ◽

Vol 34 (7) ◽

pp. 2241-2254 ◽

Cited By ~ 4

Author(s):

Gabriel Fink ◽

Sophia Burke ◽

Stefan G. H. Simis ◽

Külli Kangur ◽

Tiit Kutser ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Large Scale ◽

Lake Peipsi ◽

Management Options ◽

Scale Models ◽

Improve Water Quality

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Remote Sensing of Vegetation Recovery in Grasslands After th 1988 Fires

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1992.3115 ◽

1992 ◽

Vol 16 ◽

pp. 188-190

Author(s):

Evelyn Merrill ◽

Ron Marrs

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Landsat Imagery ◽

Snow Accumulation ◽

Vegetation Recovery ◽

Electromagnetic Spectrum ◽

Traditional Methods ◽

Burned Areas ◽

Soil Features ◽

Vegetative Biomass

Traditional methods for measurement of vegetative biomass can be time-consuming and laborÂintensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of vegetative biomass present in an area (Tucker and Sellers 1986). In 1987 we found that Landsat Multispectral Scanner (MSS) imagery could be used to quantify green herbaceous phytomass (GHP) on ungulate summer range in the northeastern portion of Yellowstone National Park. Estimates of GHP in the study area were well within values reported for the habitat types sampled (Mueggler and Steward 1980). Annual variation in GHP was related to winter snow accumulation probably due to the timing of snow melt (Merrill et al. 1988). Additionally, we found that GHP explained a significant amount of the variation in the per capita growth rate of elk population from 1972 to 1987 (Merrill and Boyce 1991). The extensive fires that occurred in the Park in the summer of 1988 provided an opportunity to determine whether remote sensing could be used to monitor grassland vegetation recovery in the Park and to explore the effects of the 1988 fires on ungulate populations using models we developed in 1987. Previous studies have used Landsat imagery to monitor succession of seral stages after fire in pine (Jakubauskas et al. 1990), but no studies to our knowledge have used this approach to quantify herbaceous recovery in grasslands. The objectives during this study period were: (1) to develop and validate a model for predicting GHP in sagebrush-grassland communities using 1989-91 Landsat TM spectral information and field data on GHP; and (2) to describe broad-scale vegetation recovery in burned areas and physiographic and soil features which influence the recovery.

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Remote Sensing of Vegetation Recovery in Grasslands After the 1988 Fires in Yellowstone National Park

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1994.3209 ◽

1994 ◽

Vol 18 ◽

pp. 114-125

Author(s):

Evelyn Merrill ◽

Ronald Marrs

Keyword(s):

Remote Sensing ◽

Yellowstone National Park ◽

National Park ◽

Field Data ◽

Large Scale ◽

Plant Cover ◽

Vegetation Indices ◽

Vegetation Recovery ◽

Electromagnetic Spectrum ◽

Burned Areas

Traditional methods for measurement of vegetative characteristics can be time-consuming and labor-intensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, one alternative for monitoring vegetation is to use remotely sensed spectral data (Tueller 1989). Spectral indices developed from field radiometric and Landsat data have been used successfully to quantify green leaf area, biomass, and total yields in relatively homogeneous fields for agronomic uses (Shibayama and Akiyama 1989), but have met with variable success in wildland situations (Pearson et aL 1976). Interference from soils (Hardinsky et al. 1984, Huete et al. 1985), weathered litter (Huete and Jackson 1987), and senesced vegetation (Sellers 1985) have diminished the relationship between green vegetation characteristics and various vegetation indices. In 1987, we found that a linear combination of Landsat Multi-spectral Scanner (MSS) band 7 and the ratio of MSS bands 6 to 4 explained 63% of the variation in green herbaceous phytomass (GHP) in sagebrush-grasslands on ungulate summer range in the northeastern portion of Yellowstone National Park (Merrill et al. 1993). The extensive fires that occurred in the Park in the summer of 1988 provided an opportunity to determine whether remote sensing could be used to estimate green phytomass in burned areas and to monitor grassland vegetation recovery in the Park after the fires. Remote sensing has previously been used to follow succession of seral stages in pine forests (Jakubauskas et al. 1990) after burning and to monitor plant cover in tundra (Hall et al. 1980) after wildfires. The objectives of our study were to: (1) develop a model for predicting GHP in sagebrushÂ grassland communities using Landsat TM spectral information and field data on GHP for 2 years, (2) validate the model by comparing predictions made from the model to actual field data collected in a third year, and if successful (3) compare initial vegetation recovery in burned areas relative to unburned sagebrush-grassland.

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Remote Sensing of Vegetation Recovery in Grasslands after the 1988 Fires

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1991.3031 ◽

1991 ◽

Vol 15 ◽

pp. 227-230

Author(s):

Evelyn Merrill ◽

Ron Marrs

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Landsat Imagery ◽

Snow Accumulation ◽

Vegetation Recovery ◽

Electromagnetic Spectrum ◽

Traditional Methods ◽

Winter Snow ◽

Summer Range ◽

Vegetative Biomass

Traditional methods for measurement of vegetative biomass can be time-consuming and laborÂintensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of vegetative biomass present in an area (Tucker and Sellers 1986). In 1987 we found that Landsat Multispectral Scanner (MSS) imagery could be used to quantify green herbaceous phytomass (GHP) on ungulate summer range in the northeastern portion of Yellowstone National Park. Estimates of GHP in the study area were well within values reported for the habitat types sampled (Mueggler and Steward 1980). Annual variation in GHP was related to winter snow accumulation probably due to the timing of snow melt (Merrill et al. 1988). Additionally, we found that GHP explained a significant amount of the variation in the per capita growth rate of elk and bison populations from 1972 to 1987 (Merrill and Boyce 1991). The extensive fires that occurred in the Park in the summer of 1988 provided an opportunity to determine whether remote sensing could be used to monitor grassland vegetation recovery in the Park and to explore the effects of the 1988 fires on ungulate populations using models we developed in 1987. Previous studies have used Landsat imagery to monitor succession of seral stages after fire in pine (Jakubauska et al. 1990), but no studies to our knowledge have used this approach to quantify herbaceous recovery in grasslands.

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Comparison of the acoustic characteristics of large-scale models of several propulsive-lift concepts

10.2514/6.1974-1094 ◽

1974 ◽

Author(s):

M. FALARSKI ◽

T. AIKEN ◽

K. AOYAGI ◽

D. KOENIG

Keyword(s):

Large Scale ◽

Acoustic Characteristics ◽

Scale Models

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MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3454009 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-11

Author(s):

Xiaochuan Tang ◽

Mingzhe Liu ◽

Hao Zhong ◽

Yuanzhen Ju ◽

Weile Li ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Large Scale ◽

Remote Sensing Image ◽

Disaster Risk ◽

Multiple Instance Learning ◽

Remote Sensing Images ◽

Loess Area ◽

Remote Sensing Image Classification ◽

Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060384 ◽

2021 ◽

Vol 10 (6) ◽

pp. 384

Author(s):

Javier Martínez-López ◽

Bastian Bertzky ◽

Simon Willcock ◽

Marine Robuchon ◽

María Almagro ◽

...

Keyword(s):

Remote Sensing ◽

Protected Areas ◽

Large Scale ◽

Anthropogenic Activities ◽

Global Scale ◽

Area Network ◽

Biophysical Characterization ◽

Challenges And Opportunities ◽

Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

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Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

Remote Sensing ◽

10.3390/rs13112220 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2220

Author(s):

Yanbing Bai ◽

Wenqi Wu ◽

Zhengxin Yang ◽

Jinze Yu ◽

Bo Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Large Scale ◽

Learning Algorithms ◽

Flood Events ◽

Test Set ◽

Source Data ◽

Flood Disasters ◽

Temporary Water ◽

Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

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