A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward toward improved characterization of evolving hazards and risk across HMA

Environmentally Tuned Conservation of Adaptive Biomolecular Condensation

Cells must be able to survive a wide array of physiological stresses posed by their environment. Primordial stresses such as heat induce conserved, robust transcriptional changes across the tree of life to help cells adapt to the new environment. However, mechanisms for transcriptional activation remain incompletely characterized. Additionally, recent work has identified that reversible, specific, and adaptive stress-induced biomolecular condensates form during heat shock, but their function remains unknown. Because each response occurs near-instantaneously with the onset of increased temperature, we hypothesized that the condensation response may serve to activate the transcriptional response. In this work I characterize the growth, transcription, and condensation responses spanning three thermal niches and nearly 100 million years of evolutionary time to quantify the conserved relationship between temperature, condensation, and transcription. Through in vitro characterization of two individual condensing proteins, we show that condensation is genetically encoded and calibrated to the environment at which the source organism is adapted. We show that stress-induced biomolecular condensation is adaptive, conserved, integrated with the growth and transcriptional responses, and tuned to features of the cellular and organismal environment to initiate at niche-specific levels

Natural Resources Research Institute Technical Report

To support guidance for the development of experimental designs for the Monticello Ecological Research ' Station (MERS) artificial streams, historical databases have been compiled, and the spatial and temporal variability of physical and biological measurements have been quantified. Databases have been constructed of chemical and biological variables measured during the course of experiments in the MERS streams conducted over a 15-year period, 1975-1989 (Table 1; USEPA and Univ. of MN 1990). Data from these experiments were supplemented with water, quality monitoring data collected by Northern States Power (NSP) near their cooling water intake on the Mississippi River at Monticello during 1968-1987 (NSP 1987) . This water intake for the Monticello nuclear power plant also serves as the main source of water for the eight experimental stream channels. Water quality data collected from the Mississippi River at the bridge on Highway 25 in Monticello by the Minnesota Pollution Control Agency (MPCA) monitoring program also are available (Appendix A), but have not been included in datasets or summaries included in this report. Data collected during the course of experiments at MERS have been selectively collated to document the natural variability within and among the MERS experimental streams; thus only pretreatment data, data collected from control channels, or data collected at inlets of control or experimental streams (upstream of chemical additions) have been included. Only data sets with sufficient spatial replication to calculate coefficients of variation for among-channel, among-station (within channel) and within station variability were included. This report describes the format and documentation for these databases, provides summary statistics for spatial and temporal variability in the MERS datasets, and discusses the implications of inter-channel, interstation (pool or riffle), and intra-station variability for future experimental designs at the MERS facility

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively. Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification