Spatial autocorrelation signatures of ecological determinants on plant community characteristics in high Andean wetlands

Lozada, Adriana; Bertin, Angéline

doi:10.1038/s41598-022-18132-9

Cited by 6 publications

(4 citation statements)

References 93 publications

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“…Moran's I analysis was used to explore spatial correlation and clustering patterns of plant diversity (Lozada & Bertin, 2022). Global Moran's I analysis, drawing upon geostatistical theory, is employed to assess the overall spatial clustering of plant diversity (Moran, 1950).…”

Section: Methodsmentioning

confidence: 99%

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Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Chen,

Hou,

Zhang

et al. 2023

Land Degrad Dev

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The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor‐intensive manual methods, which are difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α‐diversity index on a sub‐meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semiautomate large‐scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry‐standard supervised classification algorithms (support vector machine [SVM], maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible‐band difference vegetation index, soil‐adjusted vegetation index, modified soil‐adjusted vegetation index, and excess green–excess red) were used to assess vegetation diversity obtained from on‐ground survey plot data (the Margalef, Pielou, Simpson, and Shannon–Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine‐scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

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Section: Methodsmentioning

confidence: 99%

“…Moran's I analysis was used to explore spatial correlation and clustering patterns of plant diversity (Lozada & Bertin, 2022). Global…”

Section: Diversity Assessmentmentioning

confidence: 99%

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Chen,

Hou,

Zhang

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

Section: Diversity Assessmentmentioning

confidence: 99%

Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

Chen

Hou

Zhang

et al. 2023

Preprint

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The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labour intensive manual methods which can be difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α-diversity index on a sub-meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semi-automate large-scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry-standard supervised classification algorithms (support vector machine (SVM), maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (NDVI, DVI, VDVI, SAVI, MSAVI, EXG - EXR) were used to assess vegetation diversity obtained from on-ground survey plot data (Margalef, Pielou, Simpson, Shannon indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy 84%). Significant relationships were observed between vegetation indices and diversity indices, with DVI performing significantly better than many more commonly used indices such as NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine-scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

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“…The approach employed the ArcPy technique, incorporating a series of kernel files that consider local shapes and weights in addition to the scale [48][49][50][51][52]. The tool executes a neighborhood operation, where the value of each output cell is determined by the values of all the input cells within a specified neighborhood [53][54][55][56].…”

mentioning

confidence: 99%

A Computational Geospatial Approach to Assessing Land-Use Compatibility in Urban Planning

Mansourihanis,

Maghsoodi Tilaki,

Yousefian

et al. 2023

Land

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Amidst rapid urbanization, sustainable development requires moving beyond subjective land-use planning techniques toward innovative computational geospatial models. This paper introduces a GIS-based quantitative framework to enable objective, rigorous land-use compatibility analysis. Uniquely, the model evaluates radial impacts and expert-defined criteria across multiple scales, overcoming the limitations of qualitative approaches. Cell-by-cell computation identifies emerging spatial conflicts with enhanced realism. A case study in Qaemshahr, Iran, demonstrated the model’s proficiency in revealing incompatibilities and hotspots, surpassing conventional methodologies. Quantitative analysis provided accurate, transparent insights for evidence-based planning and consistency in evaluation. Ongoing improvements through 3D, real-time data integration and machine learning will further the objectivity. While extensive testing across diverse urban contexts is still needed, this pioneering computational technique marks a transition from subjective to objective methodologies. Situated at the intersection of geographic information science and urban planning, this study serves as a launchpad for advancing robust geospatial models to shape more equitable, resilient urban futures amidst complex sustainability challenges. The development of rigorous computational techniques remains fundamental, and the present innovative model can be used to provide objective, scientifically grounded compatibility analyses to guide land-use planning.

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Spatial autocorrelation signatures of ecological determinants on plant community characteristics in high Andean wetlands

Cited by 6 publications

References 93 publications

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

A Computational Geospatial Approach to Assessing Land-Use Compatibility in Urban Planning

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