Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

Ghavidel, Marzieh; Bayat, Peyman; Farashiani, Mohammad Ebrahim

doi:10.17221/56/2020-jfs

Cited by 3 publications

(2 citation statements)

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“…A zero means no vegetation, however a high NDVI value, +1 (0.8-0.9) indicates healthy vegetation and a low NDVI value, -1 indicates less or no vegetation. These indices would help to develop a method for measuring plants in different conditions (Ghavidel et al, 2021). After preparing a database of precipitation and vegetation indices, the Pearson correlation coefficient was used to evaluate the degree of correlation (invariance), and the Hotspot Spatial Autocorrelation Index (GI *) and the Moran's Index (I) were used to correlate the spatial pattern.…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…The amplitude of changes in the SAVI index for aquatic and non-vegetated areas is the same as for the NDVI index from 1 to 0, for areas with unhealthy vegetation from 0 to 0.15 and for areas with healthy vegetation from 0.15 to 1; by the way, L factor is set at 0.5 (Vani, Mandla 2017). This parameter reduces the field-effect index and reflects a lower plant cover percentage (Ghavidel et al, 2021). The normalized difference index vegetation indicates how many green leaves exist (Robinson et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin

Safa

Zarkesh

Ejlali

et al. 2021

int.jour.sci.res.mana.

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This study aimed to investigate the spatial autocorrelation between precipitation and vegetation indices in the Bandar Abbas basin. For this purpose, the vegetation indices of DVI, EVI, IPVI, NDVI, NDWI, RVI, SAVI, TCI, VCI, and VHI were derived from Landsat satellite images over 20 years were studied. Precipitation data corresponding to rain gauge stations was extracted. The Pearson correlation coefficient and the GI * and I indices were used to investigate the relationship between precipitation and spatial autocorrelation. Moreover, the Pearson correlation coefficient was used to investigate the relationship between precipitation and vegetation indices, and the GI * and I indices was used to correlate spatial autocorrelation patterns. The results showed that SAVI, VHI, VCI, and NDWI were most correlated with precipitation among the Bandar Abbas basin's vegetation indices, with the SAVI index being more closely correlated than the others. However, precipitation had the least impact on the TCI index. The spatial autocorrelation of rainfall with the vegetation indices, except for the IPVI index, had a scattered pattern in the study area’s southern and eastern parts. Of the indices studied in terms of spatial pattern, the IPVI and NDWI indices formed a positive spatial correlation pattern with precipitation over a greater spatial range.

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Section: Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin

Safa

Zarkesh

Ejlali

et al. 2021

int.jour.sci.res.mana.

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Mapping dead understoreyBuxus hyrcana Pojarkusing Sentinel-2 and Sentinel-1 data

Saba

Latifi

Zoej

et al. 2022

Forestry: An International Journal of Forest Research

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The Hyrcanian Forests comprise a continuous 800-km belt of mostly deciduous broadleaf forests and are considered as Iran’s most important vegetation region in terms of density, canopy cover and species diversity. One of the few evergreen species of the Hyrcanian Forests is the box tree (Buxus), which is seriously threatened by box blight disease and box tree moth outbreaks. Therefore, information on the spatial distribution of intact and infested box trees is essential for recovery monitoring, control treatment and management. To address this critical knowledge gap, we integrated a genetic algorithm (GA) with a support vector machine (SVM) ensemble classification based on the combination of leaf-off optical Sentinel-2 and radar Sentinel-1 data to map the spatial distribution of box tree mortality. We additionally considered the overstorey species composition to account for a potential impact of overstory stand composition on the spectral signature of understorey defoliation. We consequently defined target classes based on the combination of dominant overstorey trees (using two measures including the relative frequency and the diameter at breast height) and two defoliation levels of box trees (including dead and healthy box trees). Our classification workflow applied a GA to simultaneously derive optimal vegetation indices (VIs) and tuning parameters of the SVM. Then the distribution of box tree defoliation was mapped by an SVM ensemble with bagging using GA-optimized VIs and radar data. The GA results revealed that normalized difference vegetation index, red edge normalized difference vegetation index and green normalized difference vegetation index were appropriate for box tree defoliation mapping. An additional comparison of GA-SVM (using GA-optimized VIs and tuning parameters) with a simple SVM (using all VIs and user-based tuning parameters) showed that our suggested workflow performs notably better than the simple SVM (overall accuracy of 0.79 vs 0.74). Incorporating Sentinel-1 data to GA-SVM, marginally improved the performance of the model (overall accuracy: 0.80). The SVM ensemble model using Sentinel-2 and -1 data yielded high accuracies and low uncertainties in mapping of box tree defoliation. The results showed that infested box trees were mostly located at low elevations, low slope and facing north. We conclude that mortality of evergreen understorey tree species can be mapped with good accuracies using freely available satellite data if a suitable work-flow is applied.

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The Large-Scale Investigation and Analysis of Lophodermium piceae in Subalpine Areas Based on Satellite Multi-Spectral Remote Sensing

et al. 2022

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Picea asperata, a common tree species in the subalpine areas of Li County, Sichuan Province, China, is susceptible to Lophodermium piceae. Remote sensing has the advantages of large-scale, fast information acquisition, and low cost, which can overcome the shortcomings of ground survey. Hence, we used Landsat 8 satellite multi-spectral images and forest resource distribution data to investigate and analyze this forest disease at a large scale. Firstly, we extracted the spatial distribution information of Picea asperata and chose a temporal sequence indicator to establish a regression model and obtained a significantly negative correlation between the damage degree of plants and the change rate of normalized difference vegetation index (NDVI). Accordingly, the investigation results of the disease have good consistency with the ground survey data in spatial distribution and damage degree. On this basis, a temporal regression analysis was performed by combining the remote sensing investigation results with climate variables, and canonical correspondence analysis (CCA) was utilized in the spatial comprehensive analysis of Lophodermium piceae with terrain, soil and forest stand factors. Conclusively, this study effectively coped with the difficulties in full investigation and analysis of Lophodermium piceae in ecologically fragile subalpine areas of Western Sichuan. It is of important reference value in the early warning and monitoring of this disease, and also provides objective and reliable information support for ecological restoration and management planning in the Wenchuan earthquake-stricken areas.

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Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

Cited by 3 publications

References 20 publications

The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin

The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin

Mapping dead understoreyBuxus hyrcana Pojarkusing Sentinel-2 and Sentinel-1 data

The Large-Scale Investigation and Analysis of Lophodermium piceae in Subalpine Areas Based on Satellite Multi-Spectral Remote Sensing

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