Potential of Space-Borne Hyperspectral Data for  Biomass Quantification in an Arid Environment:  Advantages and Limitations

Zandler, Harald; Brenning, Alexander; Samimi, Cyrus

doi:10.3390/rs70404565

Cited by 19 publications

(18 citation statements)

References 38 publications

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“…Like the chlorophyll content, biomass is another challenging biophysical index for Hyperion in heterogeneous environments such as low vegetation cover areas [87,88]. The same observation was made for S2 data [89].…”

Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 49%

“…Results with HyspIRI were slightly above those of S2 because of the higher spectral resolution and the narrower bandwidth of the hyperspectral sensor, but they both revealed comparable performances. Nonetheless, Hyperion data provided better results than multispectral sensors for discriminating between NPV and soil (i.e., ETM+ sensor) [88] or for estimating indices capturing the SWIR spectral region (i.e., OLI sensor) [87]. Agricultural studies also pointed out that Hyperion is useful to detect crop residues thanks to the SWIR region that is sensitive to lignin and cellulose [92,93].…”

Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 99%

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Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 49%

Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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“…This makes Sentinel 2 an interesting alternative to commercial satellites like RapidEye in the context of woody biomass estimations in semi-arid areas [95]. In addition, hyperspectral satellite data may reduce model uncertainties for satellite-based vegetation analysis in drylands, as the higher spectral resolution is more capable in capturing the non-photosynthetic part of wood plants [27].…”

Section: Model Uncertaintiesmentioning

confidence: 99%

“…Image texture discriminates the spatial variability of neighboring pixels independent from image tone [35]. The review of existing scientific literature has shown that little research has been conducted and published on biomass or wood volume estimation in semi-arid regions using high resolution imagery in combination with indices, including the red edge band or texture attributes, e.g., [10,27,36,37].…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have shown that the performance is limited when using common broadband Vis, such as NDVI (Normalized Differenced VI) to estimate biomass or wood volume in arid to semi-arid regions using optical data with low resolution (>1 km), e.g., [15][16][17][18][19][20][21], and medium resolution (>5 m-<1 km), e.g., [9,13,[22][23][24][25]. To overcome this methodological barrier and to find proxies that could improve the accuracy in retrieving biomass information in sparsely-vegetated areas, different techniques and sensors have been used and tested and include combining high-resolution satellite data with multi-or hyper-spectral information [6,10,26,27]. Nonetheless, clear recommendations on the suitability of specific sensors for semi-arid to arid environments are lacking, and the development of an operational technique that is consistently accurate and reproducible still remains challenging [6,13].…”

Section: Introductionmentioning

confidence: 99%

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Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

et al. 2016

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Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation.

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From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

et al. 2018

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In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 9 5 m 2 ) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 9 30 m 2 ) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data-surface method, which uses a weighting layer, and regression method, which applies a regression model-with one method without auxiliary data-cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

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Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

Cited by 19 publications

References 38 publications

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

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