Trends, periodicities and ENSO relationship of New Zealand rainfall

The rainfall series for six homogeneous regions of New Zealand for 1901-1996 were not well intercorrelated (maximum correlation +0.6). Rainfalls were almost equally spread in all months. Trends (total changes over about 90 years) were ~0, +11, +2, -6, +1, +8 (±~4)% for the six regions. For seasonal rainfall, large trends were -19% for DJF and +16% for MAM of region 1. Spectral analysis showed peaks in QBO (Quasi-biennial oscillations, 2-3 years) range and near 3, 4-5, 6-9, 10-11 years and higher periodicities. ENSO relationships were not clear-cut. In individual El Niño events, only the very strong events of 1972-73, 1982-83 and 1997-98 were associated with widespread droughts in New Zealand, while the 1940-41 El Niño event was associated with excess rainfall. During the durations of all other El Niño events, New Zealand rainfalls were excess or deficit for a few months, followed by deficit or excess for the next few months (oscillatory nature), similar in all regions in some events, dissimilar in others, with no preference for any season. During La Niña (anti-El Niño) events also, oscillations were observed.

Assessment of meteorological settings on air quality modeling system - a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Region (RJMR), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well-represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes, so that the largest SO2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Characterization of global wildfire burned area spatiotemporal patterns and underlying climatic causes

Wildfires are widespread disasters and are concurrently influenced by global climatic drivers. Due to the widespread and far-reaching influence of climatic drivers, separate regional wildfires may have similar climatic cause mechanisms. Determining a suite of global climatic drivers that explain most of the variations in different homogeneous wildfire regions will be of great significance for wildfire management, wildfire prediction, and global wildfire climatology. Therefore, this study first identified spatiotemporally homogeneous regions of burned area worldwide during 2001–2019 using a distinct empirical orthogonal function. Eight patterns with different spatiotemporal characteristics were identified. Then, the relationships between major burned area patterns and sixteen global climatic drivers were quantified based on wavelet analysis. The most significant global climatic drivers that strongly impacted each of the eight major wildfire patterns were identified. The most significant combinations of hotspots and climatic drivers were Atlantic multidecadal Oscillation-East Pacific/North Pacific Oscillation (EP/NP)-Pacific North American Pattern (PNA) with the pattern around Ukraine and Kazakhstan, El Niño/Southern Oscillation-Arctic Oscillation (AO)-East Atlantic/Western Russia Pattern (EA/WR) with the pattern in Australia, and PNA-AO-Polar/Eurasia Pattern-EA/WR with the pattern in Brazil. Overall, these results provide a reference for predicting wildfire and understanding wildfire homogeneity.

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

Does the El Niño-Southern Oscillation Impact on the Indian Summer Monsoon 1-Dimensional? Quantifying the Role of Antecedent Southwestern Indian Ocean Capacitance on the Variability of Summer Monsoon Rainfall over Homogeneous Regions of India

Recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. On the other hand, the role of ocean variability is at a focal point in improving the forecasting at different time scales. Here we study the effect of Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) on Indian summer monsoon rainfall during 1993-2019. While SSTA and MSLA have been increasing in the southwestern Indian Ocean (SWIO), these parameters' large-scale variability and pre-monsoon winds could impact the inter-annual Indian monsoon rainfall variability over homogeneous regions. Similarly, antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability from an oceanic perspective. Though both SSTA and MSLA over SWIO have been influenced by El Niño-southern oscillation (ENSO), the impact of SWIO variability was low on rainfall variability over several homogeneous regions. However, rainfall over northeast (NE) and North India (NI) has been moulded by ENSO, thus changing the annual rainfall magnitude. Nevertheless, the impact of ENSO on monsoon rainfall through SWIO variability during the antecedent months is moderate. Thus, the ENSO influence on the atmosphere could be dominating the ocean part in modulating the inter-annual variability of the summer monsoon. Analysis shows that the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period.

Concepts and Revised Models for Phanerozoic Orogenic Gold Deposits

Existing published models for orogenic gold deposits (OGDs) do not adequately describe or explain most deposits of Phanerozoic age, and there are numerous reasons why Phanerozoic OGDs might differ significantly from older deposits. We subdivide Phanerozoic OGDs into four main subtypes, based on a number of descriptive criteria, including tectonic setting, lithological siting, and characteristics of the mineralization in each subtype. The four subtypes are: 1) crustal scale fault associated (CSF) subtype, 2) sediment-hosted orogenic gold (SHOG) subtype, 3) forearc (FA) subtype, and 4) syn- and late tectonic dispersed (SLTD) subtype. Lead isotopic studies suggest that Pb and other metals in all but the FA subtype were likely derived from relatively small source reservoirs in the middle or upper crust. OGDs formed in large, lithologically and structurally homogeneous regions will tend to be of the same subtype; however, in geologically complex orogenic belts it is common to find two or more subtypes that formed at approximately the same time. Based on the synthesis of global OGDs of Phanerozoic age districts containing CSF or SHOG subtype deposits appear to have the best potential for hosting multiple large deposits. FA subtype deposits form in a relatively uncommon tectonic setting (accretionary forearc, possibly overlying a subducting spreading ridge) and are likely to be rare. SLTD subtype OGDs are the most common, but most are small and uneconomic, although they commonly generate substantial alluvial gold deposits.

Geoprocessing and Landscape Ecology for Assessment Fragmentation and Connectivity of the Habitats of the Microregion of Ceres, Goiás (Brazil)

This study aimed to map and evaluate the evolution of habitat fragmentation between 2009 and 2018, using the Microregion of Ceres (Goiás) as a sample reference, using principles of Landscape Ecology. The methodology comprised the mapping of the fragments in the two years analyzed, using the OLI/Landsat 8 sensor, using scenes 222/70 and 222/71. The SPRING 5.2 software was used, where the supervised classification was performed, applying the semi-automatic process. The computational algorithm applied to classify the scenes was Maxver, which classifies pixel by pixel and groups the information of each one into homogeneous regions. After extracting the fragments of native vegetation, the methodology proposed by Juvanhol et al. (2011), in which the fragments were grouped into classes: Very Small (MP) ≤5 hectares; Small (P) ≥5.01 and ≤10 hectares; Medium (M) ≥10.01 and ≤100 hectares and Large (G) ≥100.01 hectares. For the analysis based on metrics in Landscape Ecology, the ArcGis 9.2 Patch Analyst extension was used. The results showed the expansion of vegetation cover areas in the study area, concentrated on tops of hills, APP and legal reserves. However, they pointed out intense fragmentation of native vegetation, which hinders the performance of fragments as habitats. It is considered that, from the contemporary problem of degradation of natural environments to the detriment of economic development, studies like this are necessary in order to identify existing environmental problems and propose strategies to minimize and mitigate ecological imbalances.

A New Method for Arabic Text Detection in Natural Scene Images

Text in scene images can provide useful and vital information for content-based image analysis. Therefore, text detection and script identification in images are an important task. In this paper, we propose a new method for text detection in natural scene images, particularly for Arabic text, based on a bottom-up approach where four principal steps can be highlighted. The detection of extremely stable and homogeneous regions of interest (ROIs) is based on the Color Stability and Homogeneity Regions (CSHR) proposed technique. These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting.

Multipopulation Particle Swarm Optimization for Evolutionary Multitasking Sparse Unmixing

Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary multitasking can be employed to take advantage of the implicit parallelism from different regions. In this paper, a novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the hyperspectral sparse unmixing problem. First, we resort to evolutionary multitasking optimization to cluster the hyperspectral image into multiple homogeneous regions, and directly process the entire spectral matrix in multiple regions to avoid dimensional disasters. In addition, we design a novel multipopulation particle swarm optimization method for major evolutionary exploration. Furthermore, an intra-task and inter-task transfer and a local exploration strategy are designed for balancing the exchange of useful information in the multitasking evolutionary process. Experimental results on two benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art sparse unmixing algorithms.