Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived From Ndvi Images

Silveira, Eduarda Martiniano de Oliveira; Mello, José Márcio de; Júnior, Fausto Weimar Acerbi; Reis, Aliny Aparecida dos; Withey, Kieran Daniel; Ruiz, Luis Á.

doi:10.1590/01047760201723042370

Cited by 6 publications

(3 citation statements)

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“…Image resolutions determine what wetland features can be inferred from remotely sensed imagery, and increased resolution is key to understanding spatial dependence and variability in spatial datasets (Curran, 1988). Higher image resolutions have helped to characterize landscape spatial heterogeneity among broad land uses like urban developments, agriculture, and forest (Garrigues et al, 2006;Silveira et al, 2017) and to detect features such as tree crowns and canopy gaps in mangrove ecosystems (Kamal et al, 2014). Understanding the presence and scale of relevant habitat characteristics is essential to mapping aboveground biomass in any ecosystem (Phinn et al, 1996).…”

Section: Discussionmentioning

confidence: 99%

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Doughty

Ambrose

Okin

et al. 2021

Remote Sens Ecol Conserv

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Coastal wetland biomass is an important indicator of wetland productivity, carbon storage, health, and vulnerability to climate change. The ability to estimate aboveground biomass (AGB) in wetlands at ecologically relevant scales is complicated by the spatial variability inherent to patterns in wetland vegetation and the biogeomorphic processes that help create them. Remote sensing provides an approach for mapping wetland biomass, but the spatial resolutions of satellite and airborne imagery often constrain the types of ecological patterns and processes that can be detected. Unmanned Aerial Vehicles (UAVs) have previously been used to capture fine-scale (≤1 m) variability in AGB in coastal wetland settings. However, it remains unclear if a UAV approach to estimating wetland biomass is transferrable across diverse wetland sites or how it compares to commonly used satellite-based approaches. Here, we test the capabilities of UAVs in remotely quantifying AGB and compare biomass estimation using UAV and Landsat satellite imagery (30 m resolution) in several wetland sites in Southern California. Field surveys highlight significant spatial variability in wetland plant community AGB and height that influence remote biomass estimation. Relationships between UAV vegetation indices and AGB were site-specific and influenced by vegetation types. Biomass estimation using UAVs (r 2 = 0.40, RMSE = 534.6 g m −2 ) showed better correlation with NDVI than a Landsatbased approach (r 2 = 0.26, RMSE = 596.8 g m −2 ). We found combining highresolution UAV AGB maps and Landsat NDVI to develop AGB models showed the highest correlation (r 2 = 0.45, RMSE = 659.7 g m −2 ) and provided additional spatial information to aid scaling field data to satellite imagery. Overall, UAVs captured more spatial complexity in aboveground biomass at finer scales than is possible with moderate-resolution Landsat pixels, indicating that UAVs can be used to characterize patterns of within-marsh variability resulting from local-scale (≤ 100s of meters) ecological processes.

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Section: Discussionmentioning

confidence: 99%

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Doughty

Ambrose

Okin

et al. 2021

Remote Sens Ecol Conserv

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“…Additionally, it also around this number of samples that the MAE curve tended to flatten out or lessen for both datasets. Because the spatial variability could vary based on the surface characteristics and imaging resolution utilized (Silveira et al, 2018), this number should only be used as an example for our study area and not as a guideline for other areas.…”

Section: Number Of Samples Needed To Quantify Variabilitymentioning

confidence: 99%

Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

Salas

Subburayalu

Slater

et al. 2021

Heliyon

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Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images.

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“…The change detection using the object-based approach instead of the pixel one, allows the incorporation of spatial information, such as indices derived from semivariograms (a geostatistical tool) to both analyse spatial heterogeneity (Silveira et al 2017a) and improve the LULCC without the need of using a dense time series (Silveira et al 2018a). Balaguer et al (2010) created a set of indices extracted from the semivariogram using high spatial resolution images.…”

Section: Introductionmentioning

confidence: 99%

Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

et al. 2019

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The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm.

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Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived From Ndvi Images

Cited by 6 publications

References 42 publications

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

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