Episodic fluviolacustrine deposits of Indus River Basin: a case study of Spituk palaeolake sequence

Abstract HKT-ISTP 2013 B

Palaeolake reconstruction along River Indus in Ladakh, NW India: a climate tectonics perspective

Abstract HKT-ISTP 2013 B

A mixed spectral and spatial Convolutional Neural Network for Land Cover Classification using SAR and Optical data

International audienceToday, both SAR and optical data are available with good spatial and temporal resolutions. The two data modalities complement each other in many applications. There are numerous approaches to process the two data modalities, separately or combined. Domain or modality specific approaches such as polarimetric decomposition techniques or reflectance based techniques cannot work with the two datasets combined together. Data fusion approaches incur information loss during the process and are highly application specific. Machine learning (ML) approaches can operate on the combined dataset but have their own advantages and disadvantages. There is a need to explore new ML based approaches to achieve higher performance. Convolutional neural networks (CNNs) are young, trending, and promising ML tools in remote sensing applications. CNNs have the capability to learn complex features exclusively from data. Data from the two modalities can thus be brought together and processed with increased performance. In this paper an attempt is made to analyze CNN capabilities to perform land cover classification using multi-sensor data. SAR data used in this study is L band fully polarimetric PALSAR 2 data with 6 meter spatial resolution. Three basic polarimetric bands, namely, HH, HV, and VV, and four derived bands (polarization signatures) are used. Six multispectral Landsat 8 bands, pan sharpened and resampled at 6 meter spatial resolution, are used as optical data. All 13 features are stacked together and fed as input data to the proposed CNN. The areas selected for study are Haridwar and Roorkee regions of northern India. This study introduces a CNN where convolution is performed both spatially and spectrally. We show how this is an advantage over performing only spatial convolution. Five land cover classes namely, urban, bare soil, water, dense vegetation, and agriculture are considered. The CNN is trained on more than 1200 ground truth class data points measured directly on the terrain. The classification shows results with good generalization. Comparison with other classifiers such as SVMs shows that the proposed approach provides better classification results in terms of generalization, although the cross-validation accuracy is on the same order. The evaluation of the generalization of the classified image is done using ground truth knowledge on selected subset areas in the study area

Knowledge Transfer Yields Dividend for the NSF Science and Technology Centers Program

Atlanta Conference on Science and Innovation Policy 2011This paper presents the performance and policy questions raised in a recent review of the STC program by the American Association for the Advancement of Science (AAAS). Specifically, the AAAS study assessed of the value added in each of five objectives of the STC program: research, education, diversity, knowledge transfer and integrative partnerships. The AAAS review analyzed the work of the 17 centers funded in three cohorts: 2000, 2002, 2005-06. It is the first evaluation of the STC program since 1995. Several sources of evidence were employed by the research team to triangulate the findings. These ranged from self-reported accomplishments by the centers to primary data collected from site visits and surveys. The presentation here will focus on the study’s exploration of the knowledge transfer objective of the STCs, with respect to its mechanisms, value-added and contributions to human and structural capacity building to promote excellence in science and engineeringNational Science Foundation (U.S.

Mid-Late Holocene climatic variations in Trans-Himalaya, Ladakh, NW India

Abstract HKT-ISTP 2013 B

Women Talking about Water: Feminist Subjectivities and Intersectional Understandings

In this study based on discussions held by women\u27s groups across Canada on water challenges and interests, we recognized that in the current context in Canada, women are truly connected with peoples, humans or any other form of life. They recognize that water is socially embedded, integrating issues of social, ecological and intergenerational justice in relation to complex changes in riparian landscapes. Clearly their talk is from a gender perspective, but we also found movement beyond gender that nuanced cross-sectoral understanding, critical links between gender, class and ethnicity are frequently mentioned

A mixed spectral and spatial Convolutional Neural Network for Land Cover Classification using SAR and Optical data

International audienceToday, both SAR and optical data are available with good spatial and temporal resolutions. The two data modalities complement each other in many applications. There are numerous approaches to process the two data modalities, separately or combined. Domain or modality specific approaches such as polarimetric decomposition techniques or reflectance based techniques cannot work with the two datasets combined together. Data fusion approaches incur information loss during the process and are highly application specific. Machine learning (ML) approaches can operate on the combined dataset but have their own advantages and disadvantages. There is a need to explore new ML based approaches to achieve higher performance. Convolutional neural networks (CNNs) are young, trending, and promising ML tools in remote sensing applications. CNNs have the capability to learn complex features exclusively from data. Data from the two modalities can thus be brought together and processed with increased performance. In this paper an attempt is made to analyze CNN capabilities to perform land cover classification using multi-sensor data. SAR data used in this study is L band fully polarimetric PALSAR 2 data with 6 meter spatial resolution. Three basic polarimetric bands, namely, HH, HV, and VV, and four derived bands (polarization signatures) are used. Six multispectral Landsat 8 bands, pan sharpened and resampled at 6 meter spatial resolution, are used as optical data. All 13 features are stacked together and fed as input data to the proposed CNN. The areas selected for study are Haridwar and Roorkee regions of northern India. This study introduces a CNN where convolution is performed both spatially and spectrally. We show how this is an advantage over performing only spatial convolution. Five land cover classes namely, urban, bare soil, water, dense vegetation, and agriculture are considered. The CNN is trained on more than 1200 ground truth class data points measured directly on the terrain. The classification shows results with good generalization. Comparison with other classifiers such as SVMs shows that the proposed approach provides better classification results in terms of generalization, although the cross-validation accuracy is on the same order. The evaluation of the generalization of the classified image is done using ground truth knowledge on selected subset areas in the study area

Permuted Spectral and Permuted Spectral-Spatial CNN Models for PolSAR- Multispectral Data based Land Cover Classification

International audienceIt is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization

Gender, climate change, agriculture, and food security: a CCAFS training-of-trainers (TOT) manual to prepare South Asian rural women to adapt to climate change

This training-of-trainers manual is designed to train you to be able to deliver a capacity enhancement workshop (CEW) to rural women on climate change and gender. It has been designed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and is appropriate to the South Asian context

Proliferative role of Kv11 channels in murine arteries

K+ channels encoded by the ether-a-go-go related gene (ERG1 or KCNH2) are important determinants of the cardiac action potential. Expression of both cardiac isoforms (ERG1 and ERG1b) were identified in murine portal vein and distinctive voltage-gated K+ currents were recorded from single myocytes. The aim of the present study was to ascertain the expression and functional impact of ERG channels in murine arteries. Methods: Quantitative RT-PCR was undertaken on RNA extracted from a number of murine arteries. Immunofluorescence was performed on single vascular smooth muscle cells using antibodies against the ERG1 expression product (Kv11.1). Single cell electrophysiology was performed on myocytes from portal vein and several different arteries, complimented by isometric tension recordings. Proliferation assays were undertaken on smooth muscle cells isolated from femoral arteries. Results: ERG1 transcripts were detected in all murine blood vessels, and Kv11.1 immunofluorescence was observed in all smooth muscle cells. However, K+ currents with properties consistent with ERG channels were only recorded in portal vein myocytes. Moreover, ERG channel blockers (E4031 or dofetilide, 1µM) failed to depolarise carotid arteries or produce contraction. Proliferation of arterial smooth muscle cells was associated with a marked increase in ERG1 expression and ERG blockers suppressed proliferation significantly. Conclusions: These data reveal that arterial blood vessels express ERG channels that appear to be functional silent in contractile smooth muscle but contribute to proliferative response