Exploring the spontaneous contribution of Claude E. Shannon to eco-evolutionary theory

Rodríguez, Ricardo A.; Herrera, Ada M.; Quirós, Ángel; Fernández-Rodríguez, M. J.; Delgado, Juan D.; Jiménez‐Rodríguez, Antonia; Fernández‐Palacios, José María; Otto, Rüdiger; Escudero, Carlos G.; Luhrs, Tomás Camarena; Miranda, Jezahel V.; Navarro-Cerrillo, Rafael M.; Perdomo, María Elena; Riera, Rodrigo

doi:10.1016/j.ecolmodel.2015.12.021

Cited by 10 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Information entropy is a quantitative measurement of the uncertainty of an outcome in a probabilistic event [46]. It has been widely used in ecological complexity and sustainability studies, as well as in remotesensing image classification [47][48][49][50]. Thus, the SEVI was considered for improvement using topographic slope and information entropy of the vegetation index to auto-detect typical rugged blocks (subareas) and achieve the best adjustment factor from a block where the SEVI obtains the highest information entropy in an entire scene, which is better than the manual selection of a rugged subarea for comparison of the coefficient of correlation using the r-algorithm.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

et al. 2022

View full text Add to dashboard Cite

It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of −0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used.

show abstract

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Information theory and entropy-related indicators provide insights for complex system analysis, which have high relevance in the analysis of landscape ecology [9] . Shannon entropy is a quantitative measure of the diversity of categorical data, which constitutes the core of information theory [10] .…”

Section: Introductionmentioning

confidence: 99%

Exploring spatial entropy in the analysis of landscape pattern and ecological sustainability in watershed ecosystem

Liu

Zhao

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Ecological sustainability research from the perspective of entropy has received extensive attention in recent years. Spatial entropy ( Hs ) is a new form of spatial heterogeneity analysis tool that applies entropy to distinguish and characterize different landscape patterns. In order to further explore the heterogeneity expression and ecological implication of Hs in the analysis of watershed ecosystem, this research takes Yanhe watershed as the study area. Based on the land use data of years 2000, 2010, and 2020, Hs in combination with 10 landscape metrics was applied for the study of landscape pattern and ecological sustainability. The results showed that: 1) Hs is correlated with landscape richness, aggregation and shape complexity, and it reflects the comprehensive heterogeneity information with high sensitivity to landscape variations, and also describes ecosystem stability with negative correlation; 2) The value of Hs showed a gradual increase from upstream to downstream in Yanhe watershed, and it dropped as a whole during the 20 years; 3) The stability of this basin from 2010 to 2020 had a greater variation range compared with the previous ten years, and the regions with large variation were mainly distributed in the midstream and downstream areas.

show abstract

“…Among all of the fields of natural sciences, the field of information theory and entropy-related metrics have provided the deepest insights into complex system analysis, and have high relevance in landscape ecology [ 10 , 11 , 12 , 13 ]. Descriptions of landscape patterns, dynamics of ecological processes, and interactions of pattern-process across scales in space and time are all constrained by entropy and the second law of thermodynamics [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity Analysis: Introducing a New Form of Spatial Entropy

Wang

Zhao

2018

Entropy

View full text Add to dashboard Cite

Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different landscape patterns from an entropy view are still lacking. The overall aim of this research is to propose a new form of spatial entropy ( H s ) in order to distinguish and characterize different landscape patterns. H s is an entropy-related index based on information theory, and integrates proximity as a key spatial component into the measurement of spatial diversity. Proximity contains two aspects, i.e., total edge length and distance, and by including both aspects gives richer information about spatial pattern than metrics that only consider one aspect. Thus, H s provides a novel way to study the spatial structures of landscape patterns where both the edge length and distance relationships are relevant. We compare the performances of H s and other similar approaches through both simulated and real-life landscape patterns. Results show that H s is more flexible and objective in distinguishing and characterizing different landscape patterns. We believe that this metric will facilitate the exploration of relationships between landscape patterns and ecological processes.

show abstract

Exploring the spontaneous contribution of Claude E. Shannon to eco-evolutionary theory

Cited by 10 publications

References 41 publications

Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

Exploring spatial entropy in the analysis of landscape pattern and ecological sustainability in watershed ecosystem

Spatial Heterogeneity Analysis: Introducing a New Form of Spatial Entropy

Contact Info

Product

Resources

About