scholarly journals Spatial-temporal analysis of coastline changes around Bohai Sea based on remote sensing in recent 20a

2014 ◽  
Author(s):  
Chong You ◽  
Zhiqiang Gao ◽  
Jicai Ning
Keyword(s):  
Remote Sensing ◽  
Bohai Sea ◽  
Temporal Analysis ◽  
Spatial Temporal Analysis

scholarly journals Gross Primary Production and False Spring: a spatio-temporal analysis

2020 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Raúl Zurita-Milla ◽  
Álvaro Moreno-Martinez ◽  
Gustau Camps-Valls ◽  
Anja Klisch ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Damage Index ◽  
Temporal Analysis ◽  
Environmental Stressors ◽  
Impact Of Climate Change ◽  
False Spring ◽  
Spatial Temporal Analysis

<p>Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.</p><p>Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1<sup>st</sup>). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.</p><p>Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.</p>

scholarly journals Sustainable use of an extractivist reserve: a spatial-temporal analysis of environmental dynamics

2017 ◽  
Vol 4 (7) ◽  
pp. 195-201
Author(s):  
Joélia Natália Bezerra da Silva ◽  
Janaína Vital de Albuquerque ◽  
Luana de Oliveira Rodrigues
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Vegetation Indices ◽  
Sustainable Use ◽  
Temporal Analysis ◽  
Conservation Unit ◽  
Climatic Regions ◽  
Cardisoma Guanhumi ◽  
Surrounding Areas ◽  
Spatial Temporal Analysis

Due to its large territory, Brazil has different climatic regions, which determines biome variations and equally diverse ecosystems, of this variety of vegetal landscapes, accompanies the diversity of climates. In this context, results of studies carried out locally, which guide measures, decision-making laws and regulations that reach large scales in the territory, need to be carefully planned, because there is a high risk of disregarding environmental specificities of the studied areas. Therefore, this study aimed to analyze the environmental dynamics resulting from the impacts of the last decades that have affected the habitat of the guaiamum (Cardisoma guanhumi) in the Acaú-Goiana Extractivist Reserve (RESEX) and surrounding areas. The analysis of the spatial-temporal dynamics, in the RESEX and adjacent areas, was made from the vegetation indices (SAVI) through remote sensing. In this way, three images of the RESEX were analyzed, two from the year 2010 and one from 2015, in which the RESEX was already in full legal operation. It is noticeable that there are some areas within the Conservation Unit with small plots of exposed soil, which can demonstrate the occurrence of fires.

scholarly journals Análise Espaço-Temporal da Cobertura Vegetal através do IVDN no Bairro de Boa Viagem, Recife-PE e Entorno (Spatial-Temporal Analysis of Vegetation Through the NDVI in District of Boa Viagem, Recife-PE and Surroundings)

2011 ◽  
Vol 4 (3) ◽  
pp. 575
Author(s):  
Joelma Cristine Figueiredo de Oliveira ◽  
Tiago Henrique de Oliveira ◽  
Josiclêda Domiciano Galvíncio
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Satellite Images ◽  
Temporal Analysis ◽  
Landsat Tm ◽  
Remote Sensing Technique ◽  
Spatial Temporal Analysis

Neste trabalho foram identificadas e analisadas, atraves do uso de tecnica de sensoriamento remoto e imagens de satelite Landsat TM e Quick Bird as mudancas do IVDN no bairro de Boa Viagem, Recife/ PE e seu entorno.Os resultados evidenciaram uma reducao na cobertura vegetal da area em estudo. Palavras-Chave: Mudancas na vegetacao, Quickbird, Landsat.   Spatial-Temporal Analysis of Vegetation Through the NDVI in District of Boa Viagem, Recife-PE and Surroundings   ABSTRACT In this work we identified and analyzed through the use of remote sensing technique and satellite images Landsat TM and Quickbird NDVI changes in district of Boa Viagem, Recife / PE. The results showed a reduction in vegetation cover in study area.  Keywords: Vegetation change, Quickbird, Landsat.

Spatial - Temporal Analysis of the Use and Land Cover in the Rio da Cruz Micro Basin of the Semi-arid Region of Paraíba, Using Remote Sensing

2019 ◽  
pp. 1-13
Author(s):  
Denize Monteiro Dos Anjos ◽  
Ivonete Alves Bakke ◽  
Ewerton Medeiros Simões ◽  
Olaf Andreas Bakke ◽  
Diógenes Félix da Silva Costa
Keyword(s):  
Remote Sensing ◽  
Arid Region ◽  
Satellite Images ◽  
Temporal Analysis ◽  
Relevant Information ◽  
Remote Sensing Techniques ◽  
Semi Arid Region ◽  
Bodies Of Water ◽  
Spatial Temporal Analysis ◽  
Semi Arid

The changes that occur in ecosystems are increasingly coming from anthropogenic actions. In microbasins, these changes become more noticeable and can be detected using remote sensing techniques. The Rio da Cruz microbasin, meso-region of Sertão Paraibano. Field visits were made to identify the vegetation cover and forms of land use. Then, satellite images of the three-year rainy and dry periods were used: 2001, 2009 and 2017. The following steps were performed, image processing: pre-processing; processing and post-processing. Seven classes were selected: Arboreal Caatinga, Arboreal Shrub Caatinga, Anthropized Caatinga, Pastures and Agriculture, Rocky Outcrops, Water Bodies and Buildings. The results demonstrated an advance of the antropic action in the areas near the bodies of water. The temporal analysis of the watershed of the River of the Cross allowed to verify the reduction of the Arboreal Caatinga and increase of the Arboreal Shrub Caatinga, Anthropized Caatinga and Pasture and Agriculture areas in the studied years. Remote sensing techniques and knowledge of the microbasin result in relevant information on the use and cover of the land in years of regular precipitation and in conditions of greater precipitation, the arboreal vegetation is overestimated, making it difficult to identify anthropic areas during the rainy season.

scholarly journals Spatial-Temporal Analysis of LU/LC Classification in Nirmal Mandal, Adilabad, Telangana State, India, by Using Remote Sensing and GIS

2017 ◽  
Vol 08 (11) ◽  
pp. 1315-1331 ◽  
Author(s):  
Dr T. Madhu ◽  
D. Naresh Kumar ◽  
D. Niteesh Reddy ◽  
P. V. E. Ravi Teja ◽  
L. Narayana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Analysis ◽  
Remote Sensing And Gis ◽  
Telangana State ◽  
Spatial Temporal Analysis
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