Recent Deforestation Pattern Changes (2000–2017) in the Central Carpathians: A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

Ciobotaru, Ana Maria; Andronache, Ion; Ahammer, Helmut; Jelinek, Herbert F.; Radulović, Marko; Pintilii, Radu-Daniel; Peptenatu, Daniel; Drăghici, Cristian-Constantin; Simion, Adrian Gabriel; Papuc, Razvan Mihail; Marin, Marian; Radu, Roxana Andreea; Grecu, Alexandra; Gruia, Andreea Karina; Loghin, Ioan Vlad; Fensholt, Rasmus

doi:10.3390/f10040308

Cited by 15 publications

(11 citation statements)

References 54 publications

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“…This is the first time that Pyramid Dimension and Cube Counting are analyzed for the study of forests. GLCM has also been used in the analysis of forests in the Central Group of Eastern Carpathians in Romania [46]. Compared to the previous study, our results showed that the degree of forest and deforestation disorder in the Curvature Carpathians is higher than that of the Central Group.…”

Section: Discussioncontrasting

confidence: 55%

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

et al. 2019

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The mountain ecosystems face significant damage from deforestation and environmental forest changes. We investigated the evolution of tree types of cover areas, deforested areas and total deforested areas from Curvature Carpathians using Gray-Level Co-occurrence Matrix and fractal analysis. The forest dynamics mapping was one of the main objectives of this study and it was carried out using multiple fractal and GLCM indices. We approached the analysis of satellite forest images by calculation of four fractal indices such as Pyramid dimension, Cube Counting Dimension, Fractal Fragmentation-Compaction Index and Tug-of-War lacunarity. We also calculated fractal dimension because it is an index of complexity comparing how the detail in a pattern changes with the scale at which it is measured. Fractal dimension is useful for estimation of irregularity or roughness of fractal and natural objects that do not conform to Euclidian geometry. While the fractal dimension quantifies how much space is occupied, the Tug-of-War lacunarity complements fractal dimension with its ability to quantify how space is occupied. Analysis was further supplemented by the Gray-Level Co-occurrence Matrix analysis because it quantifies spatial probability distributions of gray level values between pixel pairs within an image. The calculated Gray-Level Co-occurrence Matrix features included Angular Second Moment, Contrast, Correlation, Inverse Difference Moment and Entropy. Such comprehensive analysis has the advantage of combining fractal analysis that extracts quantitative information about the morphological complexity of the image with the spatial distribution of the gray pixel intensities as calculated by the co-occurrence features provided by Gray-Level Co-occurrence Matrix. Evolution of deforested areas, expansion of agricultural land and the increased demand for quality timber have affected the forests ecosystems and, the regional sustainable development of local communities.

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Section: Discussioncontrasting

confidence: 55%

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

et al. 2019

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“…Complete and updated national scale cartography of harvesting practices is currently missing, as it is not produced for the study area nor at a national scale [15,79]. Besides, the results obtained can be used in the future to develop and adapt forestry management policies to ensure sustainable management of exploited forest areas [80,81].…”

Section: Discussionmentioning

confidence: 99%

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Esteban¹,

Fernández-Landa²,

Tomé³

et al. 2021

Remote Sensing

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Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.

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“…The dynamics in the regional profile require detailed research regarding the impact on the other components of the territorial systems that have these economic sectors. Research on quantifying the multiplication effects of drug trade on the local economy can be analyzed with a series of algorithms, which have resulted in correlative analyzes of the relationships between the components of a territorial system [14], [15], [16], [17], [18], [19], [20].…”

Section: Discussionmentioning

confidence: 99%

The Spatial Dimension of the Drug Trade in Romania

Popescu¹,

Grecu²,

Gruia³

et al. 2020

Proceedings

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The world pharmaceutical market is constantly growing. This market is the major element of foreign investments in health. It is considered that the pharmaceutical industry is known as the most profitable industry with research and development activities. Pharmaceutical products are the most important health related products, which are traded, accounting for 55% of the total health trade. The study follows the spatial design of the dynamics of drug trade, an economic activity with a particular dynamic influenced by a complex of factors. The research was carried out starting from a database that includes the number of employees, turnover, profit and number of companies in this field, for the period 2000-2018. In order to obtain the spatial dynamics of the drug trade, a series of cartographic models were developed regarding the four indicators, using the open-source QGis platform. The obtained results showed the complex of determinants of regional disparities in the trade in medicines, the most important being the level of economic development at the level of territorial administrative unit, as well as the regional demographic particularities.

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Recent Deforestation Pattern Changes (2000–2017) in the Central Carpathians: A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

Cited by 15 publications

References 54 publications

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

The Spatial Dimension of the Drug Trade in Romania

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