The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)


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Detecting tree water deficit by very low altitude remote sensing

The team found a strong relationship between the observed functional diversity patterns and environmental factors such as soil and topography, with lower diversity on the mountain ridge under harsher environmental conditions, where the trees adapted to the dry, steep, shallow and rocky soils. Since the methodology is only limited by the availability of advanced technological sensors, this work paves the way for future airborne and satellite missions aiming at monitoring global plant functional diversity from space. Materials provided by University of Zurich.

Note: Content may be edited for style and length. Science News.

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New method to study whole forest ecosystems from above Plant functional diversity can directly be measured by mapping selected morphological and physiological traits of a forest from above. Functional traits indicate activity and health status of trees With airborne laser scanning, the scientists measured morphological characteristics of the forest canopy such as canopy height, foliage and branch densities. Observed diversity patterns consistent with topography and soil The researchers validated their method by comparing the results with leaf-level field measurements, species-level plot inventory data and databases providing functional trait values.

Potential to assess functional diversity from space "With remote sensing, we are now able to measure and monitor the diversity of forests, allowing us to observe changes at large scales and providing spatial information for nature conservation and climate change mitigation strategies," Michael Schaepman emphasizes. Story Source: Materials provided by University of Zurich. Journal Reference : Fabian D. Petchey, Andreas Hueni, David S. Schimel, Michael E.


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Mapping functional diversity from remotely sensed morphological and physiological forest traits. Because the technology can scan very large areas, it is opening up many remote and formerly inaccessible regions. It is also allowing researchers to study biodiversity and track its changes on a national scale, not just locally. The data in all four areas are invaluable to scientists and land managers because shifts in these areas can signal shifts in biodiversity.

As well, BioSpace enables scientists to study many species at once without travelling to different locations, making the process of monitoring more affordable. Generally, the level of vegetation stress around the Lahnaslampi mine is low, i. Although talc is not the only source of contamination, it can be assumed that there is strong correlation between Ni concentration and forest stress level.

Geographic location of the test area Kuosmanen et al. The data acquisition was part of the EU-funded MINEO project, which aimed at the development advanced methods for the extraction of information and knowledge from earth observation data.

This study utilized imagery from the HyMap airborne hyperspectral scanner recorded at 28th of July During the data acquisition cloud cover was non-existent. The HyMap sensor collects reflected solar radiation in bands covering the wavelengths from to nm. This includes visible, near infrared and short-wave infrared regions of the electromagnetic spectrum. In order to remove non-forest pixels from hyperspectral data, a mask image was constructed. Masked pixels were not accounted when test results were calculated. Non-forest pixels were detected by calculating the forest discrimination index FDI.

All pixels whose FDI-value was under certain threshold were removed from hyperspectral data. It is quite difficult to separate different types of green vegetation reliably. Therefore it was necessary to remove pixels representing green agricultural fields and grasslands manually when they were located near the sites where moss samples were collected. High albedo of the ore minerals causes an atmospheric scattering halo effect around the mining area, a halo which is clearly visible in HyMap imagery especially at VNIR-wavelengths.

In order to avoid the influence of halo effect on the test results, 50 meters wide buffer zone was masked around bright ore material. The mask image used to remove non-forest pixels is shown on the left panel of Figure 2. During the flight campaign 51 moss samples were collected from the test area around Lahnaslampi talc mine. Moss samples were collected by hand using contamination-free gloves. Measurements of nickel concentration were utilized in this case study. Sample sites were divided into six classes according to measured nickel concentration as follows:.

It is safe to assume that Ni concentration varies slowly within immediate neighborhood of the sampling site. Circular neighborhoods radius 50 m were created around each sampling site. These circular areas were color coded according to their Ni concentration levels. Resulting color coded map was used as a ground truth map in the testing process of Vegetation Indices. Color coded ground truth map is shown on the right panel of Fig 2.

Correlation between the values of VIs and the measured Ni concentration as well as separability of the classes were considered as the evaluation criteria for Vegetation Indices. Both measures were calculated for each VI using all pixels situated within the 50 m circular neighborhood around the sample sites.

Computation of tested VIs is described in section 5. Separability between two classes was calculated using the following simple and robust measure. Separability values for the vegetation indices are shown in table 1. In general, correlation and separability values obtained for greenness VIs were relatively high. Highest correlation and separability values were obtained for the NDVI index.

Test results of water content indices were rather poor. Among these indices best separability of classes was achieved by the MSI index.

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The value of the WBI index is almost the same for all classes; this can clearly be seen from Figure 3 where the median as well as upper and lower quartiles values are shown for each class. Leaf pigment indices that measure the carotenoids and anthocyanins concentration produced quite good test results. VIs designed to provide an estimate of the amount of carbon, i.

The test results of VIs used to estimate light use efficiency varied a lot. Most of the tested VIs could not detect Ni contaminated forest areas with adequate accuracy. From this we can correctly conclude that North East is the prevailing wind direction in the area as the dust emitted from the talc plant and mine is transported by the wind. Poor separability of classes was the common problem for almost all VIs.

This can be seen clearly in Figure 3. The major cause of poor separability is the mixed pixel problem. One pixel is usually representing canopy, shadow and soil. In this Boreal test site the forest is so dense that soil is not present in the pixels normally. The basic problems of the VIs i. When Fig 4. This is due to non-linearity and high-end saturation of the NDVI index. It is understandable because Ni contamination is not assumed to increase carbon content of the tree unless the contamination level is very high.

VIs estimating canopy water content had only very weak correlation with Ni concentration. Water absorption regions of hyperspectral data are very difficult to measure as often data in those regions is just noise. Light use efficiency indices produced mixed results. A probable cause of this is the high noise level in one channel in the blue region of the spectrum. Test results of SIPI index were not excluded from this study, but they should be interpreted with cautious mind.

As an overall conclusion from this study it can be said that the deterioration of forest health due to Ni contamination can be detected using carefully selected VIs. The objective of this study was to evaluate how well defoliated tree crowns can be separated from healthy tree crowns using different vegetation indices.

Pest inflicted stress is globally one of the most important threats to forest health. Pest inflicted damages can be sometimes avoided or reduced with proper countermeasures. Therefore it is important to be able to monitor and detect early stage pest damages in forest areas. The test area constituted a sand soiled boreal forest area in South-West Finland. Forest in the test area is dominated by Pine with some occasional Spruce and Birch trees. In the summer of a seriously damaged pine forest area was found in South-West Finland.

Studies showed that the cause of the damage was the great webspinning pine sawfly acantholyda posticalis. Great webspinning pine sawflies have been found in Finland before, but they have not caused any forest damage before. This time recent warm weathers and sandy soil of the area have made it possible for the sawflies to reproduce and cause damage.


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It is very possible that forest damage caused by great webspinning pine sawfly will be more frequent due to climate change. The tree mortality in the area was so high that is was necessary to clear cut 30 hectares of forest.

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Monitoring biodiversity with remote sensing

Most of the damaged trees were successfully clear cut, but there were still some left. This study utilized data from the AISA dual airborne hyperspectral scanner recorded at 13th of July During the acquisition the cloud cover was non existent. The AISA dual spectrometer collects reflected solar radiation in bands covering the wavelengths from to nm.

This includes the visible, near infrared and short-wave infrared regions of the electromagnetic spectrum. The ground resolution of one pixel was 2. Acquisition was carried out in early spring, so deciduous trees were still without leafs. Measurement accuracy of elevation data was 0. Ground truth data was collected by doing an extensive field study in the test area.

Moderately defoliated tees were located using visual inspection. Heavily defoliated trees were excluded because in this case most of the reflected radiation actually comes from the soil. Geo-coordinates of promising candidates were recorded using a GPS-instrument. Candidates for healthy trees were also collected. Usually spectral signature imaged from the test area represents the mixture of canopy, soil, shadow and maybe some other materials too. In order to test VIs reliably it was necessary to select final tree crown using digital surface model DSM. Both data sets were linked in ENVI software and the neighborhood of each tree crown candidate were studied.

The tree crowns for which the DSM of the tree top neighborhood showed full canopy coverage in the corresponding hyperspectral pixel were chosen for VI testing. Geo-referenced DSM image is shown on the right panel Fig 7. White pixels show laser beam returns from tree tops. As a result altogether 20 hyperspectral pixels representing 10 healthy tree crowns and 10 partly defoliated tree crowns were selected for the analysis. VIs were calculated for all 20 test pixels. Mean and standard deviation values for both healthy and defoliated classes are shown in table 1.

Many of the tested VIs did not produce any meaningful results. PRI used one wavelength channel that appeared to be noisy and therefore the results for this index are not valid. Best separability value was obtained using NDVI. NDVI correlates well with reduced chlorophyll content, so its good performance could be expected the result is understandable. The objective of this study was to determine if defoliated tree crowns can be detected using vegetation indices.

Separability was calculated using standard deviation. The number of tested pixels in each class was rather small for accurate statistical analysis. The results show that some of the VIs can detect defoliated tree areas rather reliably. Results also indicated that reduced chlorophyll content and increased anthocyanoid pigment levels are good indicators of defoliation. High spatial resolution remote sensing for forestry applications has reached an almost mature phase with wide range of applications available. However, numerous opportunities and challenges remain.

The robustness of the data processing methods is one of the issues to be considered. Processing methods currently in use often need extensive calibration and adjustment for each new imaged forest area. Processing methods should be able to handle different types of forest areas in routine fashion despite the variation of climate zone, soil type or forest structure. A lot of research has still to be done to fully utilize the potential of high spatial and spectral resolution data. In the forest health assessment, most studies are characterized by a limited geographic extent concentrating on test sites were the complexity of forest environment is low.

The methods are mostly empirical and require local calibration. Expanding forest health studies over wide geographic regions is a challenge because the added complexity of varying vegetation types can hide the relatively weak signal feature associated with forest stress. It would be highly desirable to be able to use satellite data, instead of airborne data for remote sensing of individual trees. Airborne remote sensing campaigns are very costly limiting the accessibility of data.

Airborne hyperspectral instruments offer superior signal quality and spatial resolution, however, wide coverage multi-temporal forest health monitoring using airborne data is not feasible. Satellite data has the advantage of a relatively uniform illumination and view angle over large regions, thus minimizing problems associated with combining data from individual flight lines.

Monitoring biodiversity with remote sensing

Satellite-borne high spatial resolution hyperspectral data is not available at the moment, but can be anticipated in the future as the development of space technology continues. Spatial resolution of the hyperspectral imager currently in space EO1 Hyperion , at 30 m, is too coarse for studying individual trees. OrbView-4 which failed to reach orbit after launch in , would have offered band hyperspectral data at 8 m spatial resolution. Data fusion where several data sources are used together has the potential for revolutionary impact on forest health measurement.

For example, with LiDAR data it is possible to directly measure the structural attributes of trees. The data fusion of high spectral resolution LiDAR and high spectral resolution hyperspectral data can raise forest health studies on a new level: precise information on foliar chemistry pin pointed to a single tree.

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Monitoring biodiversity with remote sensing | Natural Resources Canada

Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Introduction Global forest health is declining. Causes and symptoms of deteriorated forest health In the scope of this chapter it is only possible to address the complex relationship between causes and symptoms of deteriorated forest health briefly.

The role of remote sensing in forest health monitoring First attempts to introduce aerial photographs as a remote sensing tool in forestry were made in Vegetation indices In the assessment of forest health by means of remote sensing, features called Vegetation Indices are usually extracted from the data. Greennes chlorophyll concentration VIs Greennes VIs are designed to measure the general quantity and vigor of green vegetation.

Carbon VIs Vegetation contains many types of carbon: cellulose, lignin, sugar and starch.

The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)
The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)
The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)
The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)
The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)
The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences) The Use of Remote Sensing in the Modeling of Forest Productivity (Forestry Sciences)

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