On scales and dynamics in observing the environment

Aplin, Paul

doi:10.1080/01431160500396477

Cited by 59 publications

(36 citation statements)

References 154 publications

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“…The overall accuracy of the classification was high reaching up to 70% (κ = 0.41) for 6 m and 63% (κ = 0.27) for 20 m resolution modes. The reduction in accuracy with 20 m resolution map is due to the nature of the MLC algorithm which allocates every pixel to only one class rather than the actual mixed land cover and the size of the signal resolution resulting in mixed pixels [51,56]. This also means that the level of misclassification due to the pixels of multiple land cover classes is a function of spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

et al. 2016

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Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C-and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH-and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

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Section: Discussionmentioning

confidence: 99%

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

et al. 2016

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“…The use of combined hyperspectral and LIDAR data has been shown to increase significantly the accuracy of extraction of biophysical and hybrid variables [23,25], and the idea of combining datasets of different spatial and spectral resolutions is established in the literature [26][27][28]. Such a combination may allow for a stronger definition of the relationship between forest structure and spectra [29,30]. Naturally, a combination technique will still have the drawback of higher associated costs.…”

Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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“…Ecologically, peatlands are characterised by a wide diversity and complexity in their composition and interaction between different vegetation types [21]. Peatland restoration monitoring, therefore, requires monitoring of vegetation composition on a fine spatial scale, to match the spatial scales of the processes under observation [22]. A pixel size of less than 3.45 m is recommended, in an intact upland peatland, to determine patch size in the vegetation [23].…”

Section: Introductionmentioning

confidence: 99%

Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring

2014

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Abstract:Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced OPEN ACCESSRemote Sens. 2014, 6 717 for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.

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On scales and dynamics in observing the environment

Cited by 59 publications

References 154 publications

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring

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