Satellite remote sensing to monitor species diversity: potential and pitfalls

Rocchini, Duccio; Boyd, Doreen S.; Féret, Jean‐Baptiste; Foody, Giles M.; He, Kate S.; Lausch, Angela; Nagendra, Harini; Wegmann, Martin; Pettorelli, Nathalie

doi:10.1002/rse2.9

Cited by 157 publications

(152 citation statements)

References 82 publications

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“…Detailed vegetation mapping is essential for nature conservation, agriculture, forestry and risk management purposes [1][2][3][4][5]. The success of vegetation mapping by remote sensing is derived directly from the mapping objectives and the properties of the sensor [6][7][8].…”

Section: Phenology-based Species Classificationmentioning

confidence: 99%

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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Abstract:Most recent studies relating to the classification of vegetation species on the individual level use cutting-edge sensors and follow a data-driven approach, aimed at maximizing classification accuracy within a relatively small allocated area of optimal conditions. However, this approach does not incorporate cost-benefit considerations or the ability of applying the chosen methodology for applied mapping over larger areas with higher natural heterogeneity. In this study, we present a phenology-based cost-effective approach for optimizing the number and timing of unmanned aerial vehicle (UAV) imagery acquisition, based on a priori near-surface observations. A ground-placed camera was used in order to generate annual time series of nine spectral indices and three color conversions (red, green and blue to hue, saturation and value) in four different East Mediterranean sites that represent different environmental conditions. After outliers' removal, the time series dataset represented 1852 individuals of 12 common vegetation species and annual herbaceous patches. A feature selection process was used for identifying the optimal dates for species classification in every site. The feature selection can be designed for various objectives, e.g., optimization of overall classification, discrimination between two species, or discrimination of one species from all others. In order to evaluate the a priori findings, a UAV was flown for acquiring five overhead multiband orthomosaics (five bands in the visible-near infrared range based on the five optimal dates identified in the feature selection of the near-surface time series of the previous year. An object-based classification methodology was used for the discrimination of 976 individuals of nine species and annual herbaceous patches in the UAV imagery, and resulted in an average overall accuracy of 85% and an average Kappa coefficient of 0.82. This cost-effective approach has high potential for detailed vegetation mapping, regarding the accessibility of UAV-produced time series, compared to hyper-spectral imagery with high spatial resolution which is more expensive and involves great difficulties in implementation over large areas.

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Section: Phenology-based Species Classificationmentioning

confidence: 99%

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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“…However, these surveys are time consuming, and they require important human and material resources, making them costly and limited in time and space [8,9]. Moreover, they tend to be influenced by the assessor [10], which can make the comparison between study areas difficult. Ecological field surveys are thus limited to a local scale, whereas there is an important need to monitor biodiversity over larger extents (national to international scales).…”

Section: Introductionmentioning

confidence: 99%

“…Considerable progress has been made in the remote sensing of biodiversity during the last few decades [10]. Many works are based on the Spectral Variation Hypothesis (SVH) [14,15], which assumes that the spectral heterogeneity in the image is correlated with the heterogeneity of the habitat.…”

Section: Introductionmentioning

confidence: 99%

Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation

et al. 2017

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Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy for species diversity. Here, we argue the hypothesis that the grassland's species differ in their phenology and, hence, that the temporal variations can be used in addition to the spectral variations. The purpose of this study is to attempt verifying the SVH in grasslands using the temporal information provided by dense Satellite Image Time Series (SITS) with a high spatial resolution. Our method to assess the spectro-temporal heterogeneity is based on a clustering of grasslands using a robust technique for high dimensional data. We propose new SH measures derived from this clustering and computed at the grassland level. We compare them to the Mean Distance to Centroid (MDC). The method is experimented on 192 grasslands from southwest France using an intra-annual multispectral SPOT5 SITS comprising 18 images and using single images from this SITS. The combination of two of the proposed SH measures-the within-class variability and the entropy-in a multivariate linear model explained the variance of the grasslands' Shannon index more than the MDC. However, there were no significant differences between the predicted values issued from the best models using multitemporal and monotemporal imagery. We conclude that multitemporal data at a spatial resolution of 10 m do not contribute to estimating the species diversity. The temporal variations may be more related to the effect of management practices.

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“…However, these surveys require important human and material resources, the knowledge of the assessor and a sampling strategy, which make them expensive and time consuming [14]. They are thus limited in spatial extent and in temporal frequency, limiting grassland characterization to a local scale and over a short period of time.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

et al. 2017

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This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α-Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands' management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands' discrimination. Results are compared to other existing pixel-and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

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Satellite remote sensing to monitor species diversity: potential and pitfalls

Cited by 157 publications

References 82 publications

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

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