Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests


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KEYWORDS/PHRASES

Airborne hyperspectral sensors enable mapping at the fine scales desired by land managers [25] , [26]. Hyperspectral data may provide information on how chemical and structural properties of vascular plants vary within and across ecosystems [27] , [28] and technological improvements now allow them to be used to monitor terrestrial ecosystem characteristics [29] , [30].

Hyperspectral data allow individual tree species to be identified from their signatures collected at the forest scale when using airborne sensors [31] , [32] , [33]. The addition of co-registered LiDAR data further improves performance by identifying intra and inter-canopy shadows which alter species signatures [34] , [35]. Few maps of tree biodiversity are available for Africa [37] , [38] , [39] , [40] , so our objective was to assess whether airborne hyperspectral data could be used for this purpose, focusing on canopy-tree biodiversity of a West African moist forest.

We estimated alpha-diversity Shannon-Wiener Index; [41] in 64 permanently-marked plots in Gola National Park, Sierra Leone, which were also surveyed with a high-resolution airborne spectometer. Among the wide range of modeling tools, we selected Random Forests [42] , a machine learning algorithm which handles high dimensional input with ease and has been demonstrated to function robustly [33] , [36] , [43]. In previous studies using hyperspectral data, RF has been used by [33] to discriminate tropical tree species and by [36] to analyze the species richness of a temperate montane forest in Germany.

GRPN is collaboratively managed by the Royal Society for Protection of Birds, the Conservation Society of Sierra Leone, and the Forestry Division of the Government of Sierra Leone; they provided the permits to collect the field data used in this study, and to fly over the area during airborne data acquisition. Field study did not involve endangered or protected species. The strips of hyperspectral data which have been collected over the area are shown, together with the location of field plots overlapping the strips.

The region is characterized by lowland moist evergreen forests, with some drier types in place, dominated by Fabaceae, Euphorbiaceae and Sterculiaceae families [44]. The GRNP area has been protected through conservation programs since but commercial logging, most intensively in the southern block, was carried out in — and — Recent land cover mapping highlighted the importance of the GRNP in conserving this forest from anthropogenic pressure in the surrounding areas [45]. The climate is moist tropical, with annual rainfall around — mm, a dry season from November to April coincident with leaf-off condition of some semi-deciduous tree species, and an altitude of 70— m.

Floristic information has been derived from a field survey carried out in — [46]. We selected the plots surveyed by an hyperspectral airborne campaign, excluding those located less than 1 km from the park boundary and those affected by cloud shadow in the hyperspectral data, retaining a total of 64 ground truth plots.


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The biodiversity of a particular group of organisms in a location can be quantified in terms of richness and evenness [47]. An abundance-based measurement of plant diversity, like the Shannon-Wiener Index, should reflect the structural variability of a landscape much better than species richness, because it captures differences in composition and dominance structure of a given plant community [16].

We calculated the Shannon-Wiener index for each plot, according to the formula:.

KEYWORDS/PHRASES

Due to high noise levels, all the bands out of the — nm range and four bands in the — nm range were removed, reducing the total number of bands to For each image strip, 9 to 15 MNF components were selected by visual screening and used to compute the inverse MNF and to transform the whole set of bands back to the original data space.

For each of the 0. We refer to these three datasets as a reflectance-based metrics, b derivative-based metrics and c vegetation indices. We predicted the Shannon diversity index from spectral information contained in the three alternative datasets using Random Forests RF , a machine learning algorithm employed in many different application domains [61] , [42]. To incorporate the results from the hundreds of models, RF regression uses averaging. The second is the increase of purity among the splitting groups in the process of building a decision tree if a particular feature is used.

We chose to use the first strategy to understand the relative importance of different spectral regions in correlating with biodiversity.

RF was selected after careful consideration of its advantages and shortcomings. Unlike some other tools, there is no assumption on data distribution. The OBB strategy also makes feature i.

In our data set, there are only 64 plots, which represent a relatively small sample size considering the great variety of tree species and the vast areal coverage of the study area. Thus a tool using internal estimates is well-suited. However, RF does have some well-recognised limitations. Given that it is a non-linear statistical modelling approach based on empirical data, models derived in one study region cannot be generalized to any new data sets.

Additionally, different airborne data acquisition characteristics and preprocessing steps such as atmospheric and radiometric corrections further complicates a direct reuse of certain model.

Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests

We chose RF after careful consideration, but do not claim it is necessarily the best tool, nor has comparison been made with other regression methods to show that RF provides the most accurate results. RF was implemented within the R statistics framework randomForest package; [62] using procedures followed in numerous other studies [63] , [64]. RF regression provides an estimate of the mean squared error of residuals, but this is calculated from the OOB strategy so is different from the MSE generated by least-squares regression.

Again, pseudo-R 2 is indicative, and cannot be compared directly with conventional R 2. The 64 plots contained a total of species. In the cumulated sampled area 8.

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The 15 most common species i. The species-area curve showed that the sampled area was big enough to capture most of the large-tree diversity of the site [65] Fig. The Shannon-Wiener index ranged between 0 and 2. RF indicate that the Shannon-Wiener index can be predicted to a good level of accuracy using the plot-level statistics derived from hyperspectral bands Figure 4 and Table 2.

Models fitted using the reflectance-based metrics i. Vegetation indices were very poor predictors of diversity, giving rise to negative pseudo-R 2 that indicate an inability of the models on average to explain any of the variability in biodiversity among plots The mtry and ntree for the HS metrics were set at and , respectively. The mtry and ntree for the HS 1 st derivatives were and respectively.

HYPERSPECTRAL REMOTE SENSING OF TROPICAL AND SUBTROPICAL FORESTS

When hyperspectral band metrics were used, the most important inputs were standard deviations from the green region, but contributions came from across the spectrum and for other metrics. When the derivative-based dataset was used, standard deviations from the near infrared region provided by far the highest ranking inputs, possibly due to the ability of the derivatives to suppress background signals that are prevalent in this region.

In both of these models, the most important statistical metric was standard deviation, indicating that within-plot spectral variation is most informative in explaining diversity variation.

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The y-axis represents the percentage in increase in MSE and the x-axis is the band region in nm. The West African study is the latest in a series to shows that airborne imaging spectroscopy can be effective at mapping tree diversity, particularly when recorded at high resolution [26].

Hyperspectral remote sensing of tropical and sub-tropical forests

Spectral reflectance vary greatly within individual tree crowns, between tree crowns of the same species, and are influenced by viewing geometry soil characteristics, forest vigor and the presence of liana [31] , [32] , [66] , [38] , [67]. However, these statistical analyses seem to have picked up the same signal as the naked eye would — that species-rich plots have a greater number of subtly different canopy colors than species-poor plots. In contrast, [36] had less success with lower resolution imagery in German montane forests. A better fit was obtained by the same sensors, this time at 5 m pixel size, when mapping Shannon-Wiener index within a savanna ecosystem R 2 of 0.

Among the studies based on satellite data at lower spatial resolution, one successful result has been obtained by [19] who retrieved the Shannon index with a coefficient of determination of 0. Another study directly estimating the Shannon-Wiener index in a tropical forest was realized by [18] using the Hyperion sensor in Costa Rica with 30 m pixel size. Using wavelet decomposition followed by a stepwise regression they found that the Shannon index could be predicted with a R 2 of 0.

The selected bands were those from the shortwave infrared region and one from the visible region of the spectra nm. Our results are very similar to those obtained by [18] with respect to the ecosystem under analysis, the ability to retrieve the Shannon-Wiener index, and the poor results obtained with vegetation indices. Together, these satellite and airborne based results suggest that spatial resolution is not the main key to successful mapping of biodiversity, and additional studies targeting different ecosystems are needed to clarify the relative importance of spatial and spectral resolution.

Derivative analysis might not be optimal for our aims and data, resulting in a lower R 2 Fig. A possible explanation can be attributed to the fact that derivative is very sensitive to noise in the original spectrum. The residual noise is emphasized in the derivative spectra and this may vary according to the pixel location on the tree crown. In addition, environmental or stress factors such as moisture content and leaf age introduce subtle variations in crown reflectance that are enhanced by differentiation.

Consequently, spectral variation within crowns can be unnecessarily boosted in the derivative domain, interfering with the identification of differences amongst crowns. Species richness and Shannon-Wiener index are both widely used as indices of diversity in the remote sensing literature, but we argue that the abundance weighted index is more valuable from an ecological perspective. Similarly, [36] found that species richness was the better of the two response variables in terms of goodness-of-fit for German forests.

The reason we recommend using the Shannon-Wiener index is that ecosystem processes, such as water balance and nutrient cycles, depends primarily on the functional characteristics of the most abundant species [68]. The Shannon-Wiener index is weighted in favor of abundant-species, making it more useful for relating spectral signals to local ecological processes. However, a key open question in biodiversity studies is whether information on canopy biodiversity can be a surrogate for sub-canopy biodiversity; with this respect is of interest the [34] research, which estimated the diversity of foliar chemicals within the canopy as a whole using hyperspectral data, and related this to faunal and floral distributions.

Introduction to Hyperspectral Remote Sensing: A Presentation

There is currently great interest in using airborne remote sensing to go one step further, and map individual canopy species in tropical forests [26].

Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests
Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests
Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests
Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests
Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests

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