Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data

Khare, Suyash; Latifi, Hooman; Khare, Siddhartha

doi:10.3390/rs13193965

Cited by 18 publications

(17 citation statements)

References 37 publications

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“…where min and max are the minimum and maximum values measured in winter and summer, respectively, start of season (SOS) and end of season (EOS) are the inflection points when the curve rises and falls, and slope1 and slope2 are the rates of increase and decrease of the curve at the inflection points [15,42,43]. This function describes asymmetrical patterns, leading to a reliable estimation of the trajectory in canopy greenness [42].…”

Section: Curve Fitting and Statisticsmentioning

confidence: 99%

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

et al. 2021

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Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.

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Section: Curve Fitting and Statisticsmentioning

confidence: 99%

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

et al. 2021

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“…The NDVI of the study area was calculated online using the Landsat images from 2017 to 2021 on the Google Earth Engine (GEE) [ 32 , 33 ]. The images from June 1 to September 30 in each year were selected to avoid the vegetation change caused by the season difference when the vegetation grows vigorously.…”

Section: Methodsmentioning

confidence: 99%

The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing

Guo

et al. 2023

IJERPH

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Understanding the synergistic effect between topography and vegetation in the underground coal mine is of great significance for the ecological restoration and sustainable development of mining areas. This paper took advantage of unmanned aerial vehicle (UAV) remote sensing to obtain high-precision topographic factors (i.e., digital elevation model (DEM), slope, and aspect) in the Shangwan Coal Mine. Then, a normalized difference vegetation index (NDVI) was calculated utilizing Landsat images from 2017 to 2021, and the NDVI with the same spatial resolution as the slope and aspect was acquired by down-sampling. Finally, the synergistic effect of topography and vegetation in the underground mining area was revealed by dividing the topography obtained using high-precision data into 21 types. The results show that: (1) the vegetation cover was dominated by “slightly low-VC”, “medium-VC”, and “slightly high-VC” in the study area, and there was a positive correlation between the slope and NDVI when the slope was more than 5°. (2) When the slope was slight, the aspect had less influence on the vegetation growth. When the slope was larger, the influence of the aspect increased in the study area. (3) “Rapidly steep–semi-sunny slope” was the most suitable combination for the vegetation growth in the study area. This paper revealed the relationship between the topography and vegetation. In addition, it provided a scientific and effective foundation for decision-making of ecological restoration in the underground coal mine.

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“…The flowchart of OMRF-PGAU is shown in Figure 2. For a given image I, it is firstly divided into four partition images I = I (1) , I (2) , I (3) , I (4) , which have the same size, and there are overlapping areas between every two partition images. Then the RAG, G = (V, E) for the original image I, and G = G (1) , G (2) , G (3) , G (4) ,…”

Section: The Omrf-pgau Modelmentioning

confidence: 99%

“…for the partitioned images I are established, respectively, and the feature field Y, Y = Y (1) , Y (2) , Y (3) , Y (4) , and label field X, X = X (1) , X (2) , X (3) , X (4) are formed. The category number for each image is set as K, K (1) , K (2) , K (3) , K (4) respectively.…”

Section: The Omrf-pgau Modelmentioning

confidence: 99%

“…Semantic segmentation is an important part of remote sensing image processing, and its goal is to give a special semantic interpretation to the homogeneous regions in the image. The semantic segmentation results of remote sensing images can provide a macro statistical data base for vegetation growth analysis [1], coastline protection [2], urban development [3,4], etc.…”

Section: Introductionmentioning

confidence: 99%

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An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image

et al. 2021

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The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.

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Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data

Cited by 18 publications

References 37 publications

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing

An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image

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