Ground-truth Data For Validation Research Articles

A Geospatial Analysis of Environmental Impacts of Climate Change on Osogbo and Environs

Climate change is one of the alarming global environmental changes likely to have deleterious effects on natural, social, cultural, and human systems. The risks associated with it call for a broad spectrum of policy responses and strategies at local, regional, national, and global levels. This research aims to carry out a geospatial analysis of the environmental impact of climate change on Osogbo and its environs with a view to providing information towards mitigating the negative impact of climate change in the study area. This study uses Landsat 4, Landsat 7, and Landsat 8 satellite data from 1982, 2002, and 2022, respectively, to evaluate land cover and temperature variation as a climatic change in the study area over four decades. The land survey method was carried out to collect ground truth data for validation of the satellite imageries. The drought map was produced based on the Vegetation Condition Index (VCI) map computed from Normalized Difference Vegetation Index (NDVI) datasets and was further classified into three categories: moderate, mild, and no drought. Analysis of the Land Use Land Cover (LULC) of the study area shows that the vegetation decreased from 84% in 1982 to 59% in 2002 and 56% in 2022. The built-up/exposed surface increased from 15.28% in 1982 to 38% in 2002 and 41.28% in 2022. The water bodies also increased from 0.17% in 1982 to 15.31% in 2022. LST analysis reveals that the mean temperature of the study area increased by 10.73 °C during the past four decades (from 1982 to 2022), with the most accelerated warming (11.41 °C) occurring during the last two decades (from 2002 to 2022). The Vegetation Condition Index (VCI) based drought map shows that there was no extreme or severe drought in the study area. However, moderate and mild droughts were observed mostly in the developed part of the study area, which confirmed the presence of drought. It is hereby recommended to introduce sustainable water management strategies to mitigate water shortage in the study area.

Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data

Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

Predicting post-fire canopy mortality in the boreal forest from dNBR derived from time series of Landsat data

Regulatory and certification agencies need historical fire pattern information across the Canadian boreal forest to support natural disturbance-based management. Landsat-derived spectral indices have been used extensively to map burn severity in North America. However, satellite-derived burn severity is difficult to define and quantify, and relies heavily on ground truth data for validation, which hinders fire pattern analysis over broad scales. It is therefore critical to translate burn severity estimates into more quantifiable measurements of post-fire conditions and to provide more cost-effective methods to derive validation data. We assessed the degree to which Landsat-derived indices and ancillary data can be used to classify canopy mortality for 10 fires in the boreal forest of Alberta and Saskatchewan, Canada. Models based on two and three mortality classes had overall accuracies of 91 and 72% respectively. The three-level classification has more utility for resource management, with improved accuracy at predicting unburned and complete canopy mortality classes (93 and 66%), but is relatively inaccurate for the partial mortality class (56%). The results presented here can be used to assess the suitability of different canopy mortality models for forest fire management goals, to help provide objective, consistent and cost-effective results to analyse historical fire patterns across the Canadian boreal forest.

Measuring Ocean Bottom Pressure at the North Pole

AbstractHigh-precision deep Arctic Bottom Pressure Recorders (ABPRs) were developed to measure ocean bottom pressure variations in the perennial ice-covered Arctic Ocean. The ABPRs use the tsunami detection DART acoustic modem technology and have been programmed to store and transmit the data acoustically without the need to recover the instrument. ABPRs have been deployed near the North Pole, where the ice cover is a year-round challenge for access with a ship. Instead, the ABPRs have been built as light-weight mechanical systems that we can install using aircraft landing on the ice. ABPRs have provided the first records of uninterrupted pressure data over continuous years ever made in the central Arctic. The ABPR data have allowed us to identify and understand modes of Arctic Ocean bottom pressure variability that were unknown before the ABPR records and have offered new means of investigating and understanding the rapidly changing Arctic system. The ABPR records have also shown outstanding agreement with the satellite-sensed ocean bottom pressure anomalies from GRACE, providing ground truth data for validation of the satellite system. Due to the successful science findings as well as the ABPRs' capability to fulfill the upcoming potential gaps of pressure measurements between GRACE and a GRACE follow-on mission, we highlight the urgent need to develop and maintain an Arctic observing network using ABPRs.

ＮＯＡＡ ＡＶＨＲＲを用いた北東アジア落葉樹林におけるフェノロジー観測手法の提案

Phenology is closely related to carbon, water and other material cyclings on the earth's surface, and one of the most important indicators to understand vegetation responses to climate change. Satellite data provides us with a tool to monitor and assesses spatio-temporal variations in phenology. However, phenology based on satellite observation does not validated extensively with green-up or leaves dropping date acquired by ground based observation because of the lack in ground truth data for validation. By the way, Japanese Meteorological Agency (JMA) have observed the timing of flowering, budding, leaves coloring and dropping at more than 100 meteorological stations in Japan since 1953. In this study, green-up date detected from NOAA AVHRR observation was validated with JMA's phenological data, and four algorithms to detect green-up date were evaluated. As a result, spatio-temporal variation in green-up dates detected by newly developed algorithm was highly consistent with the ground data compared to another algorithms, and RMSE between the retrieved green-up dates and in situ data was 6.6 days. And, it was found that the algorithm was able to detect responses of green-up date to changes in annual temperature.

Agricultural land monitoring using a linear mixture model

Intensive and mixed land cover areas, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different vegetation covers are critical problems facing the classification process of satellite images when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between cultivated/non‐cultivated. It has been difficult to accurately distinguish the different types of vegetation covers in areas that are within one pixel of satellite images. In this study, a linear mixture model (LMM) approach is applied to classify land covers in the eastern Nile delta of Egypt. Four endmembers (desert land, urban, cultivated land, water bodies) were identified based on the image itself and a constrained least‐square solution was used to unmix the image. Same method was applied on other three endmembers representing the main vegetation covers in the study area. Relationship between fraction images and NDVI was determined and the fraction images were compared with ground truth data for validation. This study indicates that the LMM is a promising approach for distinguishing the different land cover types and to classify the different vegetation types using Landsat ETM+ data.