Michael Schmidt | The University of Queensland, Australia (original) (raw)

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Papers by Michael Schmidt

Pasture biomass is an important quantity globally in livestock industries, carbon balances, and b... more Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r 2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture 'growth' sense. A more robust combined-season model was established with an adjusted r 2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products.

Monitoring of ground-cover is an important task for land management since it has been linked to i... more Monitoring of ground-cover is an important task for land management since it has been linked to indicators of soil loss, biodiversity, and pasture production. Ground-cover is an indicator adopted by Queensland natural resource and catchment management groups. However, accurate spatial estimation of ground-cover is confounded by varying cover types, cover greenness and soil colour. This research reports on ground-cover mapping based on spectral mixture analysis (SMA) of LANDSAT satellite imagery. Estimates of green and senescent vegetation and soil fractions are derived from iterative SMA. Correlations with field data are form SMA iterations are discussed with r 2 values of 0.78 and 0.69 respectively for bare ground estimates over black soils.

Crop extent and frequency maps are an important input to inform the debate around land value and ... more Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity.

$Research paper thumbnail of Field measurement of fractional ground cover$

This handbook has been compiled to aid systematic field observation and recording of vegetative a... more This handbook has been compiled to aid systematic field observation and recording of vegetative and non-vegetative fractional cover. It will be used by all states and the Northern Territory to establish a national network of ground cover sites in landscapes managed for grazing and broadacre cropping. The data collected as described in this handbook will be used to calibrate and validate large area fractional cover spatial datasets derived from remote sensing. These spatial datasets are produced regularly—monthly, seasonally, annually—enabling change in ground cover to be monitored consistently across Australia. Monitoring ground cover indicates the vulnerability of the land to soil erosion, biodiversity and productivity loss. This provides a valuable tool to quantify and communicate the effects of management actions. The handbook uses many site description attributes from the Australian soil and land survey field handbook (NCST 2009), which should be used in conjunction with this ha...

Ground cover is a critical attribute of the landscape affecting infiltration and can functionally... more Ground cover is a critical attribute of the landscape affecting infiltration and can functionally be related to surface runoff, nutrient fluxes and soil erosion. Ground cover relates to living and non-living materials on the soil surface. Besides the direct economic value for graziers, varying levels of ground cover have indirect economic and major ecological implications on productivity, land condition and biodiversity. Remote sensing offers one of the few ways to monitor long term ground cover change over large spatial extents. This work is supporting the recently initiated Great Barrier Reef Water Quality Protection Plan to limit negative environmental impacts to the Great Barrier Reef.

Ground cover plays an important role in Australia's environmental, agricultural and economic sust... more Ground cover plays an important role in Australia's environmental, agricultural and economic sustainability. It is effective in reducing the loss of sediments through wind and water erosion and supports a diverse range of biodiversity. The Queensland Remote Sensing Centre (RSC) in the Queensland Government has an established ground cover monitoring program. The Queensland Ground Cover Monitoring Program (QGCMP) uses satellite imagery and field data to research and develop state-wide, regional and local ground cover mapping and monitoring products. The products support government legislation, conservation efforts and land management initiatives. The QGCMP is based on the extensive archives and processing streams for Landsat and other imagery. The Program developed the Ground Cover Index (GCI) which uses ~25 years of Landsat TM/ETM+ imagery and field data to quantify ground cover levels. The resultant index was applied across Queensland with an RMSE of 12.9%. Subsequent additions and improvements to the QGCMP have included: establishment of a supporting field monitoring program including research of new field-based technologies; development of new unmixing algorithms for fractional vegetation cover; research and development of systems and methods for time-series analysis for more regular monitoring; and, cross-calibrations between sensors for deriving and modelling biomass and other ground cover metrics. This paper outlines some of the research undertaken and products developed for the QGCMP and discusses the application of the products in key state and federal government policies and initiatives.

High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities t... more High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor
and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011)
with an 8-day interval of a 30 m spatial resolution data was generated by the use of the Spatial and Temporal
Adaptive Reflectance Fusion Model (STARFM) with Landsat sensor observations and Moderate Resolution Imaging
Spectroradiometer (MODIS) data as input. The time series showed a close relationship over homogeneous
forested and grassland sites, with r2 values of 0.99 between Landsat and the closest STARFM simulated data;
and values of 0.84 and 0.94 between MODIS and STARFM. The time and magnitude of clearing and re-clearing
events were estimated through a phenological breakpoint analysis, with 96.2% of the estimated breakpoints of
the clearing event and 83.6% of the re-clearing event being within 40 days of the true clearing. The study highlights
the benefits of using these moderate resolution data for quantifying and understanding land cover change
in open forest environments.

General circulation models (GCMs) were constructed for future projections of circulation patterns... more General circulation models (GCMs) were constructed for future projections of circulation patterns on a global scale. IPCC emission scenarios, adopted by GCMs, suggest that climate change is due to anthropogenic emissions of greenhouse gases. Application of GCMs to regionalscale studies is difficult due to the different spatial resolutions. Downscaling techniques transfer GCM results to larger scales. Rainfall-triggered landslides are a worldwide phenomenon and can cause socio-economic problems. Regional models of these geomorphic processes were linked to regionalized GCM outputs for New Zealand. Climate-model outputs from HadCM2SUL were used to produce precipitation and temperature scenarios via analog downscaling. Climate-impact studies have rarely been developed for New Zealand. For both Wellington and Hawke’s Bay, climate-change
scenarios were applied to 3 deterministic landslide models (the daily rainfall model, the antecedent daily rainfall model and the antecedent soil water status model). All of them relate landslide occurrence to climate conditions. Results give a more reliable projected probability change of landslide
occurrence for Wellington than for Hawke’s Bay. Wellington’s cold-season precipitation is mostly associated with synoptic weather systems depending on large-scale circulation features, captured using the downscaling procedure. In contrast, Hawke’s Bay receives its peak precipitation from frequent high-magnitude storms. Common to all 3 applied landslide models for both regions is the trend of decreased landslide activity for the target period, 2070–2099.

Long time series of well-calibrated and consistent daily remote sensing data are important for st... more Long time series of well-calibrated and consistent daily
remote sensing data are important for studies of intra- and
inter-annual environmental behavior. These data are used
to support environmental management, and in most loca-
tions are the only historical spatial dataset. The Web- CATS
( CSIRO AVHRR Time Series) system provides access to the
Australasian Advanced Very High Resolution Radiometer
( AVHRR ) data archive using the World Wide Web. The data
archive consists of multiple daily satellite overpasses from
several receiving stations in Australia since July 1981. The
data are held online enabling the use of state-of-the-art
algorithms to generate on-demand user-customized products.
This novel design for operational and dynamic remote
sensing data product generation enables Web- CATS users to
browse the entire metadata-database and produce consistent
time series information from on-line data in near real time.
As these algorithms improve, users have the ability to easily
re-process their dataset(s).

We evaluate the geometric accuracy of Advanced Very High Resolution Radiometer (AVHRR) data proce... more We evaluate the geometric accuracy of Advanced Very High Resolution Radiometer (AVHRR) data processed
using the Common AVHRR Processing System (CAPS) software. Accurate geometric correction, to known standards, of
satellite images is crucial for many remote sensing applications, especially those using a time series of images and
integrating other spatially referenced datasets. It is critical that data acquired by satellites with short repeat intervals (e.g.,
daily) are geolocated using methods that are both automatic and reliable. Landsat Enhanced Thematic Mapper Plus (ETM+)
high-resolution imagery was used to establish ground control point (GCP) networks for two coastal regions of Australia, one
tropical with 40 GCPs and one temperate with 60 GCPs. The geolocation accuracy was assessed for a total of 12 and 13
AVHRR images, respectively, against the GCPs for each region, respectively, totalling 1039 cloud-free GCP comparisons.
Results showed that when the view zenith angle was less than 40°, the average (±1 standard deviation) accuracies are 0.36 ±
0.31 and 0.44 ± 0.35 of an at-nadir AVHRR pixel (1.1 km × 1.1 km) in the cross-track direction for the tropical and
temperate regions, respectively, and 0.30 ± 0.26 and 0.36 ± 0.29 at-nadir AVHRR pixel in the along-track direction for the
tropical and temperate regions, respectively. For higher satellite view zenith angles, the geometric accuracy decreases
systematically with an increase in the pixel size towards the edges of the AVHRR swath.

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegeta... more The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflec-tance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatio-temporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12 × 10 km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89
to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events
were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relation-ship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes
within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product (r 2 1⁄4 0.92) from the Queensland Department of Environment and Resource Management.

We have developed an automatic method to monitor the AVHRR instrument sensitivity over time in th... more We have developed an automatic method to monitor the AVHRR instrument sensitivity over time in the short-wave reflective channels to ensure that trends in the data series obtained by this instrument are real and not sensor artefacts.
Our radiometric calibration method uses the Multivariate Alteration Detection (MAD) algorithm to statistically select invariant features over land areas from multiple image-pairs that are compared to assess changes in the instrument's calibration. This method requires no a priori regional
knowledge and is globally applicable. A calibrated time series from Pseudo-Invariant Features located in central Australia are shown to have long-term trends removed. The resulting MAD-based calibration has a root mean squared error of ∼ 5–6% for both channels 1 and 2 and is in alignment with other approaches.

Aquatic weeds have caused significant problems in many lakes and river systems worldwide. Weed ou... more Aquatic weeds have caused significant problems in many lakes and river systems worldwide. Weed outbreaks of water hyacinth (Eichhornia crassipes) and para-grass (Urochloa mutica) are common in Australia and their ecological and recreational impacts mostly negative and costly. Remote sensing offers the ability to map and monitor the distribution of aquatic weeds and their early detection. The objective of this project was to develop an efficient method, using remote sensing techniques, to map and monitor the change of dense water weeds in a river system and to identify a suitable spatial scale for this process. Two SPOT (Satellite Pour l'Observation de la Terre) 5 images from May 2006 and May 2007 were used in combination with two mapping approaches on a) multispectral image data with 10 m spatial resolution and b) pan-sharpened multispectral image data with 2.5 m spatial resolution. A scale dependent validation resulted in case b) an overall producer's classification accuracy of 81%. Small outbreaks (~2 m 2 ) alone were
71% accurate with increasing accuracies of >95% for outbreaks larger than 6.25m 2 (2.5m x 2.5m pixel). Case a) generally had lower accuracies, with accuracies of >95% for
outbreaks in the order of 100m 2 (10m x 10m pixel) and larger. The results suggest that the river infestation by aquatic weeds in a test area of the mid-Brisbane River has
increased by a factor of 2 to 3 during the 12-month period. The infested area is estimated to be between 13.6% and 15.9 % of the waterbody in 2007, while 6.2% to 6.8% in 2006.
The method applied in this study included geometric and radiometric corrections, along with linear spectral unmixing and spectral angle mapper techniques. This method is
applicable to other waterways worldwide and offers the potential for the early detection of infestations of aquatic surface weeds.

$Research paper thumbnail of Field measurement of fractional ground cover$