Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

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 30m 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 r 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 40days 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

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) "Index-then-Blend" (IB); and (ii) "Blend-then-Index" (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm

Diffuse Skylight as a Surrogate for Shadow Detection in High-Resolution Imagery Acquired Under Clear Sky Conditions

An alternative technique for shadow detection and abundance is presented for high spatial resolution imagery acquired under clear sky conditions from airborne/spaceborne sensors. The method, termed Scattering Index (SI), uses Rayleigh scattering principles to create a diffuse skylight vector as a shadow reference. From linear algebra, the proportion of diffuse skylight in each image pixel provides a per pixel measure of shadow extent and abundance. We performed a comparative evaluation against two other methods, first valley detection thresholding (extent) and physics-based unmixing (extent and abundance). Overall accuracy and F-score measures are used to evaluate shadow extent on both Worldview-3 and ADS40 images captured over a common scene. Image subsets are selected to capture objects well documented as shadow detection anomalies, e.g., dark water bodies. Results showed improved accuracies and F-scores for shadow extent and qualitative evaluation of abundance show the method is invariant to scene and sensor characteristics. SI avoids shadow misclassifications by avoiding the use of pixel intensity and the associated limitations of binary thresholding. The method negates the need for complex sun-object-sensor corrections, it is simple to apply, and it is invariant to the exponential increase in scene complexity associated with higher-resolution imagery

Using Remote Sensing for Identifying Suitable Areas for Flood Shelter: A Case Study of Thatta, Sindh Pakistan

The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future

Using remote sensing to forecast forage quality for cattle in the dry savannas of northeast Australia

In the dry savannas of northeast Australia, forage quality is just as important for cattle production as forage quantity. The seasonal trend of forage quality is broadly predictable by land managers, but it is more difficult to predict the point when quality—which depends on local climate, management, and pasture condition—falls below the requirement for animal maintenance. In this study we use statistical modelling to forecast how forage quality might change at the crucial time of year, i.e., as the summer wet season transitions to the dry winter. We do this with the aid of historical information associated with a long-term cattle-grazing trial in the dry savannas. We combined multiple years of two measures of forage quality (dietary crude protein and in vivo dry-matter digestibility; respectively DCP and DMD) and ground cover information (specifically the ratio of ‘green grass’ cover to ‘dead (i.e., non-photosynthetic) grass’ cover, derived from an archive of Landsat satellite imagery) into a linear mixed model that explicitly considered the correlations with time and between variables. DCP and DMD were estimated by near-infrared spectroscopy of fresh faecal samples; values did not have to be temporally coincident with the satellite imagery. With the end of May considered a nominal decision-point, we forecast monthly averages of forage quality for June to August, over a 12-year period at the study site. Over all months and all years, the median absolute error of the forecasts was DCP = 0.86%, and DMD = 0.95%. The remote sensing information served as a correlated, oft-sampled covariate that helped to guide the forecasts of forage quality. We propose summarising the forecasts (and their uncertainty) as a near-real-time graphical tool for decision-support. Such a product could potentially benefit cattle-grazing enterprises in the northeast of Australia, enabling more timely management of herds through the dry season

Examination of Sentinel-2A Multi-spectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalize MSI Reflectance to Nadir BRDF Adjusted Reflectance

The Sentinel-2A multi-spectral instrument (MSI) acquires multi-spectral reflective wavelength observations with directional effects due to surface reflectance anisotropy and changes in the solar and viewing geometry. Directional effects were examined by considering two ten day periods of Sentinel-2A data acquired close to the solar principal and orthogonal planes over approximately 20° × 10° of southern Africa. More than 6.6 million (January 2016) and 10.6 million (April 2016) pairs of reflectance observations sensed 3 or 7 days apart in the forward and backscatter directions in overlapping Sentinel-2A orbit swaths were considered. The Sentinel-2A data were projected into the MODIS sinusoidal projection but first had to be registered due to a misregistration issue evident in the overlapping orbits. The top of atmosphere reflectance data were corrected to surface reflectance using the SEN2COR atmospheric correction software. Only pairs of forward and backward reflectance values that were cloud and snow-free, unsaturated, and had no significant change in their 3 or 7 day separation, were considered. The maximum observed Sentinel-2A view zenith angle was 11.93°. Greater BRDF effects were apparent in the January data (acquired close to the solar principal plane) than the April data (acquired close to the orthogonal plane) and at higher view zenith angle. For the January data the average difference between the surface reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges increased with wavelength from 0.035 (blue), 0.047 (green), 0.057 (red), 0.078 (NIR), to about 0.1 (SWIR). These differences may constitute a significant source of noise for certain applications. The suitability of a recently published methodology developed to generate Landsat nadir BRDF-adjusted reflectance (NBAR) was examined for Sentinel-2A application. The methodology uses fixed MODIS BRDF spectral parameters and is attractive because it has little sensitivity to the land cover type, condition, or surface disturbance and can be derived in a computationally efficient manner globally. It was applied to the southern Africa Sentinel- 2A data and shown to reduce Sentinel-2A BRDF effects. The average difference between the reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges was smaller in the NBAR data than in the corresponding surface reflectance data. Residual BRDF effects in the Sentinel-2A NBAR data occurred likely because of atmospheric correction and sensor calibration errors and inadequacies in the NBAR derivation approach. These issues are discussed with recommendations for future research including global and red-edge Sentinel-2A NBAR derivation that were not considered in this study

Dating flowering cycles of Amazonian bamboo-dominated forests by supervised Landsat time series segmentation

Bamboo-dominated forests are unusual and interesting because their structure and biomass fluctuate in decades-long cycles corresponding to the flowering and mortality rhythm of the bamboo. In southwestern Amazonia, these forests have been estimated to occupy an area of approximately 160 000 km(2), and a single reproductively synchronized patch can cover up to thousands of square kilometers. Accurate mapping of these forests is challenging, however: the forests are spatially heterogeneous, with bamboo densities varying widely among adjacent sites; much of the area is inaccessible, so field verification of bamboo presence is difficult to obtain and georeferenced records of past flowering events virtually non-existent; and detectability of the bamboo by remote sensing varies considerably during its life cycle. In this study, we develop a supervised time series segmentation approach that allows us to identify both the presence of bamboo forests and the years in which the bamboo flowering and subsequent mortality have occurred. We then apply the method to the entire Landsat TM/ETM+ archive from 1984 to the end of 2018 and validate the classification by visual interpretation of very high resolution imagery. Collecting accurate ground reference data of bamboo presence and bamboo mortality timing is notably difficult in these forests, and we therefore developed a methodology that takes advantage of imperfect reference data obtained from the Landsat time series itself. Our results show that bamboo forests can be differentiated from non-bamboo forests using any of the infrared bands, but band 5 produces the highest classification accuracy. Interestingly, there appears to be a temporal difference in the spectral responses of the three infrared bands to bamboo flowering and mortality: near infrared (band 4) reflectance reacts to the event earlier than shortwave infrared (bands 5 and 7) reflectance. The long Landsat TM/ETM+ archive allows our methodology to detect some areas with two mortality events, with a theoretical maximum interval of 29 years. Analysis of these pixels with repeated mortality confirms that the life cycles of the local bamboo species (Guadua sarcocarpa and G. weberbauerii) last typically 28 years

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

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 r2 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 r2 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. View Full-Tex

Local and regional scale drivers of arid zone small mammal population dynamics

Australia’s arid zone small mammals are primarily governed by rainfall. With extreme rainfall events often being separate by prolonged periods of drought, long term data sets (> 10 years) are generally required to study small mammal ecology. In this thesis, I leverage two long term data sets collected in arid New South Wales and South Australia to investigate drivers of small mammal population dynamics at both the local and regional scale. At the local scale, I investigate the relationship between Landsat Fractional Cover (FC) measurements to assess their potential to identify small mammal habitat. By associating FC measurements with 12 years of small mammal surveying, I find evidence Landsat FC measurements are closely related to the population dynamics of rodent species Leggadina forresti and Mus musculus but not marsupial species Sminthopsis macroura and Sminthopsis crassicaudata. This suggests that Landsat FC measurements could capture suitable habitat for small mammal species with boom-and-bust population dynamics in arid rangelands. On a regional scale, I investigate Mus musculus population synchrony throughout a roughly 25 000km2 region of the Strzelecki desert and Barrier Range. By assessing the correlation between sub-population dynamics and regional rainfall, I identify groups of synchronous sub-populations that are not spatially autocorrelated or driven by regional rainfall variability. Analysis of the synchronous groups subsequently reveals that variable predator assemblages drive regional asynchrony, suggesting that while M. musculus may be more persistent where dingoes occur, they reach greater abundances where they do not. The results from these chapters highlight how various management actions impact several arid zone small mammal populations, while also identifying key areas for future research that will assist conservation land managers in identifying and mitigating threats to vulnerable species