Object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using a Landsat 5 TM Dry-season time series

2013 ◽  
Vol 58 (1) ◽  
pp. 53-77 ◽  
Author(s):  
James Boyden ◽  
Karen E. Joyce ◽  
Guy Boggs ◽  
Penny Wurm
Keyword(s):  
Time Series ◽  
Dry Season ◽  
Native Vegetation ◽  
Object Based ◽  
Landsat 5 Tm

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

2012 ◽  
Vol 34 (7) ◽  
pp. 2432-2453 ◽  
Author(s):  
Xuexia Chen ◽  
James E. Vogelmann ◽  
Gyanesh Chander ◽  
Lei Ji ◽  
Brian Tolk ◽  
...  
Keyword(s):  
Time Series ◽  
Irs P6 ◽  
Landsat 5 Tm ◽  
Time Series Images ◽  
Disturbance Detection

scholarly journals Riparian Land Use and Hydrological Connectivity Influence Nutrient Retention in Tropical Rivers Receiving Wastewater Treatment Plant Discharge

2021 ◽  
Vol 9 ◽  
Author(s):  
N. R. Finkler ◽  
B. Gücker ◽  
I. G. Boëchat ◽  
M. S. Ferreira ◽  
M. O. Tanaka ◽  
...  
Keyword(s):  
Land Use ◽  
Wastewater Treatment ◽  
Vegetation Cover ◽  
Nutrient Retention ◽  
Dry Season ◽  
Analysis Of Covariance ◽  
Native Vegetation ◽  
Hydrological Connectivity ◽  
Riparian Areas ◽  
Nutrient Inputs

Riparian areas are recognized for their buffering capacity regarding phosphorus and nitrogen from agricultural and urban runoff. However, their role in attenuating nutrient loads of rivers receiving point source nutrient inputs (e.g., from wastewater treatment plants, WWTPs) is still little understood. Here, we investigated whether ammonium (NH4-N), nitrate (NO3-N), and soluble reactive phosphorus (SRP) retention were influenced by the riparian land use in three Brazilian rivers receiving WWTP effluents. We hypothesized that nutrient attenuation would be potentially influenced by the hydrological connectivity between the main channel and riparian areas with native vegetation. We estimated retention from longitudinal patterns of dilution-corrected nutrient concentrations below the WWTPs. We assessed nutrient retention during periods with high (i.e., the wet) and low connectivity (i.e., the dry season). Relationships between non-conservative (nutrients) and conservative (chloride) solutes in both seasons were used to identify potential changes in the river chemistry due to the hydrological connectivity with the riparian areas. We also evaluated the relationship between net uptake velocities (Vf-net) and the accumulated percent native vegetation cover in the 100-m buffer using linear regressions, comparing the response for each nutrient between seasons with Analysis of Covariance. Slopes of regressions between nutrients and chloride significantly differed between seasons for NO3-N and SRP but not for NH4-N. The relationships between Vf-net and accumulated native vegetation in the riparian buffer presented steeper slopes for SRP in the wet than in the dry season. No significant relationships between NO3-N Vf-net and native vegetation cover were observed in either season. In contrast, increases in Vf-net with increasing vegetation cover were observed for NH4-N in the dry season. In periods with expected higher connectivity, NO3-N and SRP concentrations tended to be lower relative to chloride concentrations, with a potential effect of native vegetation in the riparian area on SRP retention. Our results suggest that seasonal connectivity between nutrient-rich river water and riparian areas is likely to induce changes in the predominant nutrient transformation processes, thereby favoring either nutrient retention or export in such rivers.

scholarly journals Detection of contributing object to driving operations based on hidden Markov model

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987679
Author(s):  
Kohjiro Hashimoto ◽  
Tetsuyasu Yamada ◽  
Takeshi Tsuchiya ◽  
Kae Doki ◽  
Yuki Funabora ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Cognitive Performance ◽  
Traffic Accidents ◽  
Hidden Markov ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Cognitive Support ◽  
Object Based

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

scholarly journals Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

2019 ◽  
Vol 11 (5) ◽  
pp. 570 ◽  
Author(s):  
Inacio Bueno ◽  
Fausto Acerbi Júnior ◽  
Eduarda Silveira ◽  
José Mello ◽  
Luís Carvalho ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Seasonal Changes ◽  
Vegetation Indices ◽  
Detection Methods ◽  
Object Based Image Analysis ◽  
Spectral Bands ◽  
Object Based ◽  
Shortwave Infrared ◽  
Spectral Channels

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

scholarly journals Glacier recession and water resources in Peru’s Cordillera Blanca

2012 ◽  
Vol 58 (207) ◽  
pp. 134-150 ◽  
Author(s):  
Michel Baraer ◽  
Bryan G. Mark ◽  
Jeffrey M. McKenzie ◽  
Thomas Condom ◽  
Jeffrey Bury ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Hydrologic Model ◽  
Dry Season ◽  
Glacier Retreat ◽  
Cordillera Blanca ◽  
Tropical Glaciers ◽  
Daily Discharge ◽  
Synthetic Time Series ◽  
Nonparametric Statistical

AbstractThe tropical glaciers of the Cordillera Blanca, Peru, are rapidly retreating, resulting in complex impacts on the hydrology of the upper Río Santa watershed. The effect of this retreat on water resources is evaluated by analyzing historical and recent time series of daily discharge at nine measurement points. Using the Mann-Kendall nonparametric statistical test, the significance of trends in three hydrograph parameters was studied. Results are interpreted using synthetic time series generated from a hydrologic model that calculates hydrographs based on glacier retreat sequences. The results suggest that seven of the nine study watersheds have probably crossed a critical transition point, and now exhibit decreasing dry-season discharge. Our results suggest also that once the glaciers completely melt, annual discharge will be lower than present by 2-30% depending on the watershed. The retreat influence on discharge will be more pronounced during the dry season than at other periods of the year. At La Balsa, which measures discharge from the upper Río Santa, the glacier retreat could lead to a decrease in dry-season average discharge of 30%.

scholarly journals Air pollution and hospital admissions for respiratory diseases in the subequatorial Amazon: a time series approach

2010 ◽  
Vol 26 (4) ◽  
pp. 747-761 ◽  
Author(s):  
Eliane Ignotti ◽  
Sandra de Souza Hacon ◽  
Washington Leite Junger ◽  
Dennys Mourão ◽  
Karla Longo ◽  
...  
Keyword(s):  
Time Series ◽  
Respiratory Diseases ◽  
Hospital Admissions ◽  
Fine Particulate Matter ◽  
The Elderly ◽  
Dry Season ◽  
Daily Number ◽  
Fine Particulate ◽  
Time Series Approach ◽  
Series Study

The objective of the study is to evaluate the effect of the daily variation in concentrations of fine particulate matter (diameter less than 2.5µm - PM2.5) resulting from the burning of biomass on the daily number of hospitalizations of children and elderly people for respiratory diseases, in Alta Floresta and Tangará da Serra in the Brazilian Amazon in 2005. This is an ecological time series study that uses data on daily number of hospitalizations of children and the elderly for respiratory diseases, and estimated concentration of PM2.5. In Alta Floresta, the percentage increases in the relative risk (%RR) of hospitalization for respiratory diseases in children were significant for the whole year and for the dry season with 3-4 day lags. In the dry season these measurements reach 6% (95%CI: 1.4-10.8). The associations were sig-nificant for moving averages of 3-5 days. The %RR for the elderly was significant for the current day of the drought, with a 6.8% increase (95%CI: 0.5-13.5) for each additional 10µg/m3 of PM2.5. No as-sociations were verified for Tangara da Serra. The PM2.5 from the burning of biomass increased hospitalizations for respiratory diseases in children and the elderly.

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1467 ◽  
Author(s):  
Meghan Halabisky ◽  
Chad Babcock ◽  
L. Moskal
Keyword(s):  
Time Series ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Sampling Design ◽  
Landsat Imagery ◽  
Temporal Dimension ◽  
Substantial Impact ◽  
Object Based Image Analysis ◽  
Temporal Domain ◽  
Object Based

Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification.

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