Relating MODIS vegetation index time-series with structure, light absorption and stem production of fast-growing Eucalyptus plantations

By allowing the estimation of forest structural and biophysical characteristics at different temporal and spatial scales, remote sensing may contribute to our understanding and monitoring of planted forests. Here, we studied 9-year time-series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a network of 16 stands in fast-growing Eucalyptus plantations in Sao Paulo State, Brazil. We aimed to examine the relationships between NDVI time-series spanning entire rotations and stand structural characteristics (volume, dominant height, mean annual increment) in these simple forest ecosystems. Our second objective was to examine spatial and temporal variations of light use efficiency for wood production, by comparing time-series of Absorbed Photosynthetically Active Radiation (APAR) with inventory data. Relationships were calibrated between the NDVI and the fractions of intercepted diffuse and direct radiation, using hemispherical photographs taken on the studied stands at two seasons. APAR was calculated from the NDVI time-series using these relationships. Stem volume and dominant height were strongly correlated with summed NDVI values between planting date and inventory date. Stand productivity was correlated with mean NDVI values. APAR during the first 2 years of growth was variable between stands and was well correlated with stem wood production (r(2) = 0.78). In contrast, APAR during the following years was less variable and not significantly correlated with stem biomass increments. Production of wood per unit of absorbed light varied with stand age and with site index. In our study, a better site index was accompanied both by increased APAR during the first 2 years of growth and by higher light use efficiency for stem wood production during the whole rotation. Implications for simple process-based modelling are discussed. (C) 2009 Elsevier B.V. All rights reserved.

Compact tiled display with uniform illumination

The demand for more pixels is beginning to be met as manufacturers increase the native resolution of projector chips. Tiling several projectors still offers a solution to augment the pixel capacity of a display. However, problems of color and illumination uniformity across projectors need to be addressed as well as the computer software required to drive such devices. We present the results obtained on a desktop-size tiled projector array of three D-ILA projectors sharing a common illumination source. A short throw lens (0.8:1) on each projector yields a 21-in. diagonal for each image tile; the composite image on a 3×1 array is 3840×1024 pixels with a resolution of about 80 dpi. The system preserves desktop resolution, is compact, and can fit in a normal room or laboratory. The projectors are mounted on precision six-axis positioners, which allow pixel level alignment. A fiber optic beamsplitting system and a single set of red, green, and blue dichroic filters are the key to color and illumination uniformity. The D-ILA chips inside each projector can be adjusted separately to set or change characteristics such as contrast, brightness, or gamma curves. The projectors were then matched carefully: photometric variations were corrected, leading to a seamless image. Photometric measurements were performed to characterize the display and are reported here. This system is driven by a small PC cluster fitted with graphics cards and running Linux. It can be scaled to accommodate an array of 2×3 or 3×3 projectors, thus increasing the number of pixels of the final image. Finally, we present current uses of the display in fields such as astrophysics and archaeology (remote sensing).

Using FCD Mapper Software and Landsat Images to Assess Forest Canopy Density in Landscapes in Australia and the Philippines

Using Landsat imagery, forest canopy density (FCD) estimated with the FCD Mapper®, was correlated with predominant height (PDH, measured as the average height of the tallest 50 trees per hectare) for 20 field plots measured in native forest at Noosa Heads, south-east Queensland, Australia. A corresponding image was used to calculate FCD in Leyte Island, the Philippines and was validated on the ground for accuracy. The FCD Mapper was produced for the International Tropical Timber Organisation and estimates FCD as an index of canopy density using reflectance characteristics of Landsat Enhanced Thematic (ETM) Mapper images. The FCD Mapper is a ‘semi-expert’ computer program which uses interactive screens to allow the operator to make decisions concerning the classification of land into bare soil, grass and forest. At Noosa, a positive strong nonlinear relationship (r2 = 0.86) was found between FCD and PDH for 15 field plots with variable PDH but complete canopy closure. An additional five field plots were measured in forest with a broken canopy and the software assessed these plots as having a much lower FCD than forest with canopy closure. FCD estimates for forest and agricultural land in the island of Leyte and subsequent field validation showed that at appropriate settings, the FCD Mapper differentiated between tropical rainforest and banana or coconut plantation. These findings suggest that in forests with a closed canopy this remote sensing technique has promise for forest inventory and productivity assessment. The findings also suggest that the software has promise for discriminating between native forest with a complete canopy and forest which has a broken canopy, such as coconut or banana plantation.

A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management

Two major factors are likely to impact the utilisation of remotely sensed data in the near future: (1)an increase in the number and availability of commercial and non-commercial image data sets with a range of spatial, spectral and temporal dimensions, and (2) increased access to image display and analysis software through GIS. A framework was developed to provide an objective approach to selecting remotely sensed data sets for specific environmental monitoring problems. Preliminary applications of the framework have provided successful approaches for monitoring disturbed and restored wetlands in southern California.

Remotely sensed estimates of vegetation structural characteristics in restored wetlands, Southern California

Traditional field sampling approaches for ecological studies of restored habitat can only cover small areas in detail, con be time consuming, and are often invasive and destructive. Spatially extensive and non-invasive remotely sensed data can make field sampling more focused and efficient. The objective of this work was to investigate the feasibility and accuracy of hand-held and airborne remotely sensed data to estimate vegetation structural parameters for an indicator plant species in a restored wetland. High spatial resolution, digital, multispectral camera images were captured from an aircraft over Sweetwater Marsh (San Diego County, California) during each growing season between 1992-1996. Field data were collected concurrently, which included plant heights, proportional ground cover and canopy architecture type, and spectral radiometer measurements. Spartina foliosa (Pacific cordgrass) is the indicator species for the restoration monitoring. A conceptual model summarizing the controls on the spectral reflectance properties of Pacific cordgrass was established. Empirical models were developed relating the stem length, density, and canopy architecture of cordgrass to normalized-difference-vegetation-index values. The most promising results were obtained from empirical estimates of total ground cover using image data that had been stratified into high, middle, and low marsh zones. As part of on-going restoration monitoring activities, this model is being used to provide maps of estimated vegetation cover.

Determining forest structural attributes using an inverted geometric-optical model in mixed eucalypt forests, Southeast Queensland, Australia

The Montreal Process indicators are intended to provide a common framework for assessing and reviewing progress toward sustainable forest management. The potential of a combined geometrical-optical/spectral mixture analysis model was assessed for mapping the Montreal Process age class and successional age indicators at a regional scale using Landsat Thematic data. The project location is an area of eucalyptus forest in Emu Creek State Forest, Southeast Queensland, Australia. A quantitative model relating the spectral reflectance of a forest to the illumination geometry, slope, and aspect of the terrain surface and the size, shape, and density, and canopy size. Inversion of this model necessitated the use of spectral mixture analysis to recover subpixel information on the fractional extent of ground scene elements (such as sunlit canopy, shaded canopy, sunlit background, and shaded background). Results obtained fron a sensitivity analysis allowed improved allocation of resources to maximize the predictive accuracy of the model. It was found that modeled estimates of crown cover projection, canopy size, and tree densities had significant agreement with field and air photo-interpreted estimates. However, the accuracy of the successional stage classification was limited. The results obtained highlight the potential for future integration of high and moderate spatial resolution-imaging sensors for monitoring forest structure and condition. (C) Elsevier Science Inc., 2000.

Monitoring growth in rapidly urbanizing areas using remotely sensed data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

An assessment of the changes in the distribution and extent of mangroves within Moreton Bay, southeast Queensland, Australia, was carried out. Two assessment methods were evaluated: spatial and temporal pattern metrics analysis, and change detection analysis. Currently, about 15,000 ha of mangroves are present in Moreton Bay. These mangroves are important ecosystems, but are subject to disturbance from a number of sources. Over the past 25 years, there has been a loss of more than 3800 ha, as a result of natural losses and mangrove clearing (e.g. for urban and industrial development, agriculture and aquaculture). However, areas of new mangroves have become established over the same time period, offsetting these losses to create a net loss of about 200 ha. These new mangroves have mainly appeared in the southern bay region and the bay islands, particularly on the landward edge of existing mangroves. In addition, spatial patterns and species composition of mangrove patches have changed. The pattern metrics analysis provided an overview of mangrove distribution and change in the form of single metric values, while the change detection analysis gave a more detailed and spatially explicit description of change. An analysis of the effects of spatial scales on the pattern metrics indicated that they were relatively insensitive to scale at spatial resolutions less than 50 m, but that most metrics became sensitive at coarser resolutions, a finding which has implications for mapping of mangroves based on remotely sensed data. (C) 2003 Elsevier Science B.V. All rights reserved.

Predicting sheetwash and rill erosion over the Australian continent

Soil erosion is a major environmental issue in Australia. It reduces land productivity and has off-site effects of decreased water quality. Broad-scale spatially distributed soil erosion estimation is essential for prioritising erosion control programs and as a component of broader assessments of natural resource condition. This paper describes spatial modelling methods and results that predict sheetwash and rill erosion over the Australian continent using the revised universal soil loss equation (RUSLE) and spatial data layers for each of the contributing environmental factors. The RUSLE has been used before in this way but here we advance the quality of estimation. We use time series of remote sensing imagery and daily rainfall to incorporate the effects of seasonally varying cover and rainfall intensity, and use new digital maps of soil and terrain properties. The results are compared with a compilation of Australian erosion plot data, revealing an acceptable consistency between predictions and observations. The modelling results show that: (1) the northern part of Australia has greater erosion potential than the south; (2) erosion potential differs significantly between summer and winter; (3) the average erosion rate is 4.1 t/ha. year over the continent and about 2.9 x 10(9) tonnes of soil is moved annually which represents 3.9% of global soil erosion from 5% of world land area; and (4) the erosion rate has increased from 4 to 33 times on average for agricultural lands compared with most natural vegetated lands.

Image calibration to like-values in mapping shallow water quality from multitemporal data

The applicability of image calibration to like-values in mapping water quality parameters from multitemporal images is explored, Six sets of water samples were collected at satellite overpasses over Moreton Bay, Brisbane, Australia. Analysis of these samples reveals that waters in this shallow bay are mostly TSS-dominated, even though they are occasionally dominated by chlorophyll as well. Three of the images were calibrated to a reference image based on invariant targets. Predictive models constructed from the reference image were applied to estimating total suspended sediment (TSS) and Secchi depth from another image at a discrepancy of around 35 percent. Application of the predictive model for TSS concentration to another image acquired at a time of different water types resulted in a discrepancy of 152 percent. Therefore, image calibration to like-values could be used to reliably map certain water quality parameters from multitemporal TM images so long as the water type under study remains unchanged. This method is limited in that the mapped results could be rather inaccurate if the water type under study has changed considerably. Thus, the approach needs to be refined in shallow water from multitemporal satellite imagery.

Ecosystem process models at multiple scales for mapping tropical forest productivity

Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

The 23rd October 2002 dust storm in eastern Australia: characteristics and meteorological conditions

The dust storm of 23 October 2002 covered most of eastern Australia and carried one of the largest recorded dust loads in Australia. In the 6 months leading up to the event, severe drought conditions in eastern Australia, plus above average maximum temperatures resulted in high potential evapo-transpiration rates, producing severe soil moisture deficits and reduced vegetation cover. Although increased wind speeds associated with a fast moving cold front were the meteorological driving force, these winds speeds were lower than those for the previously documented large dust storms. The dust storm was 2400 km long, up to 400 km across and 1.5-2.5 km in height. The plume area was estimated at 840,860 km 2 and the dust load at 0900 h was 3.35-4.85 million tones (Mt). These dust load estimates are highly sensitive to assumptions, regarding visibility-dust concentration relationships, vertical dust concentration profiles and dust ceilings. The event is examined using meteorological records, remote sensing and air quality monitoring. (C) 2004 Elsevier Ltd. All rights reserved.

Mapping changes in coastline geomorphic features using Landsat TM and ETM+ imagery: examples in southeastern Brazil

Multitemporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery was used to assess coastline morphological changes in southeastern Brazil. A spectral linear mixing approach (SLMA) was used to estimate fraction imagery representing amounts of vegetation, clean water (a proxy for shade) and soil. Fraction abundances were related to erosive and depositional features. Shoreline, sandy banks (including emerged and submerged banks) and sand spits were highlighted mainly by clean water and soil fraction imagery. To evaluate changes in the coastline geomorphic features, the fraction imagery generated for each data set was classified in a contextual approach using a segmentation technique and ISOSEG, an unsupervised classification. Evaluation of the classifications was performed visually and by an error matrix relating ground-truth data to classification results. Comparison of the classification results revealed an intense transformation in the coastline, and that erosive and depositional features are extremely dynamic and subject to change in short periods of time.