Vegetation response to hydrologic and geomorphic factors in an arid region of the Baja California Peninsula

Rodríguez-Moreno, Víctor Manuel; Bullock, Stephen H.

doi:10.1007/s10661-013-3435-5

Cited by 15 publications

(6 citation statements)

References 44 publications

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“…In vegetation communities such as forest, cropland, savanna, shrubland, and grassland, the ability of NDVI time-series has been verified for the varied semi-arid and arid climates to obtain seasonal and inter-annual variability [15]. SAVI was also used for evaluating vegetation development in the semi-arid region of Mexico, which is closely related to NDVI [32,33].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Sharifi

Tahir

et al. 2021

Water

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In arid and semi-arid regions, it is essential to monitor the spatiotemporal variability and dynamics of vegetation. Among other provinces of Pakistan, Punjab has produced a significant number of crops. Recently, Punjab, Pakistan, has been described as a global hotspot for extremes of climate change. In this study, the soil adjusted vegetation index (SAVI), normalized vegetation difference index (NDVI), and enhanced vegetation index (EVI) were comprehensively evaluated to monitor vegetation change in Punjab, Pakistan. The time-series MODIS (Moderate Resolution Imaging Spectroradiometer) data of different periods were used. The mean annual variability of the above vegetation indices (VIs) from 2000 to 2019 was evaluated and analyzed. For each type of vegetation, two phenological metrics (i.e., for the start of the season and end of the season) were calculated and compared. The spatio-temporal image analysis of the mean annual vegetation indices revealed similar patterns and varying vegetation conditions. In the forests and vegetation areas with sparse vegetation, the EVI showed high uncertainty. The phenological metrics of all vegetation indices were consistent for most types of vegetation. However, the NDVI result had the greatest variance between the start and end of season. The lowest annual VI variability was mainly observed in the southern part of the study area (less than 10% of the study area) based on the statistical analysis of spatial variability. The mean annual spatial variability of NDVI was <20%, SAVI was 30%, and EVI ranged between 10–20%. More than 40% of the variability was observed in the NDVI and SAVI vegetation indices.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Sharifi

Tahir

et al. 2021

Water

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“…The SAVI, which is closely related to NDVI and can minimize the effect of soil brightness, has been used to record vegetation development for the semi-arid ecosystem in northwestern Mexico [30,31]. In addition, the MODIS-derived EVI and NDVI showed a strong linear relationship with gross primary production comparing with in situ flux measurements at a station located in the semi-arid environment of Sahel [32,33].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Kuenzer

Wang

et al. 2015

Remote Sensing

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Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series' suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001-2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

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“…These were the Topographic Wetness Index (TWI), Relative Slope Height (RSH), and Total Curvature (TC). These indices are related to water availability and landscape structure, which strongly affects vegetation distribution in arid ecosystems (Abutaha, El‐Khouly, Jürgens, & Oldeland, 2020; Rodríguez‐Moreno & Bullock, 2014). Handling of the DEM, preparation of the topographical data, and PCA were done with SAGA‐GIS (Conrad et al., 2015).…”

Section: Methodsmentioning

confidence: 99%

Do image resolution and classifier choice impact island biogeographical parameters of terrestrial islands?

Oldeland¹,

Eibes

Irl

et al. 2022

Transactions in GIS

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Island biogeography provides concepts for conservation management as fragmented habitats are comparable to ocean islands. Remote sensing can help to extract terrestrial habitat islands on the landscape scale. However, little is known about the effects of image resolution and classification algorithms on resulting island size and related parameters. We study the combined effect of three image resolutions (2, 10, and 30 m) and three classification algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) by extracting quartz islands from WorldView‐2 imagery using image segmentation. We compared four island parameters (i.e., area, distance, shape index, and perimeter–area ratio between resolutions and classifiers). We found that in all cases, image resolution and classification algorithms had a strong effect. However, image resolution was more important for area and shape. Artificial Neural Network always provided the best performance as a classifier (OA: 0.880, kappa: 0.801, F1: 0.912). Hence, conservation strategies could lead to different results when different pattern extraction strategies are applied. Future studies which aim at extracting terrestrial habitat islands from image datasets should aim for the highest possible resolution and compare the outcomes of different classifiers to ensure the best possible results.

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Vegetation response to hydrologic and geomorphic factors in an arid region of the Baja California Peninsula

Cited by 15 publications

References 44 publications

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Do image resolution and classifier choice impact island biogeographical parameters of terrestrial islands?

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