Xihua Yang

Modelling and mapping rainfall erosivity in New South Wales, Australia

Considerable seasonal and inter-annual changes exist in rainfall amount and intensity in New South Wales (NSW), Australia. These changes are expected to have significant effect on rainfall erosivity and soil erosion by water, but the magnitude of the impact is not well quantified because of the non-linear and dynamic nature of the relationship between rainfall amount and rainfall erosivity. The primary aim of this study was to model spatial and temporal variations in rainfall erosivity and impacts on hillslope erosion across NSW. We developed a daily rainfall erosivity model for NSW to calculate monthly and annual rainfall erosivity values by using gridded daily rainfall data for a continuous 53-year period including a baseline period (1961–90) and a recent period (2000–12). Model parameters were improved based on their geographic locations and elevations to be truly geo-referenced and representative of the regional relationships. Monthly and annual hillslope erosion risk for the same periods was estimated with the Revised Universal Soil Loss Equation. We produced finer scale (100-m) maps of rainfall erosivity and hillslope erosion through spatial interpolation techniques, and implemented the calculation of rainfall erosivity and hillslope erosion in a geographic information system by using automated scripts so that it is fast, repeatable and portable. The modelled rainfall erosivity values were compared with pluviograph calculations and previous studies, and the Nash–Sutcliffe coefficient of efficiency is >0.90. Outcomes from this study provide not only baseline information but also continuous estimates of rainfall erosivity and hillslope erosions allowing better monitoring and mitigation of hillslope erosion risk in NSW.

Pragmatic models for the prediction and digital mapping of soil properties in eastern Australia

To help meet the increasing need for knowledge and data on the spatial distribution of soils, readily applied multiple linear regression models were developed for key soil properties over eastern Australia. Selected covariates were used to represent the key soil-forming factors of climate (annual precipitation and maximum temperature), parent material (a lithological silica index) topography (new topo-slope and aspect indices) and biota (a modified land disturbance index). The models are presented at three depth intervals (0–10, 10–30 and 30–100 cm) and are of variable but generally moderate statistical strength, with concordance correlation coefficients in the order of 0.7 for organic carbon (OC) upper depth, pHca, sum of bases, cation exchange capacity (CEC) and sand, but somewhat lower (0.4–0.6) for OC lower depths, total phosphorous, clay and silt. The pragmatic models facilitate soil property predictions at individual sites using only climate and field-collected data. They were also moderately effective for deriving digital soil maps over the state of New South Wales and a regional catchment. The models and derived maps compared well in predictive ability to those derived from more sophisticated techniques involving Cubist decision trees with remotely sensed covariates. The readily understood and interpreted nature of these products means they may provide a useful introduction to the more advanced digital soil modelling and mapping techniques. The models provide useful information and broader insights into the factors controlling soil distribution in eastern Australia and beyond, including the change in a soil property with a given unit change in a covariate.

Delineating soil landscape facets from digital elevation models using compound topographic index in a geographic information system

Soil landscapes and their component facets (or sub-units) are fundamental information for land capability assessment and land use planning. The aim of the study was to delineate soil landscape facets from readily available digital elevation models (DEM) to assist soil constraint assessment for urban and regional planning in the coastal areas of New South Wales (NSW), Australia. The Compound Topographic Index (CTI) surfaces were computed from 25 m DEM using a D-infinity algorithm. The cumulative frequency distribution of CTI values within each soil landscape was examined to identify the values corresponding to the area specified for each unmapped facet within the soil landscape map unit. Then these threshold values and CTI surfaces were used to generate soil landscape facet maps for the entire coastal areas of NSW. Specific programs were developed for the above processes in a geographic information system so that they are automated, fast, and repeatable. The modelled facets were assessed by field validation and the overall accuracy reached 93%. The methodology developed in this study has been proven to be efficient in delineating soil landscape facets, and allowing for the identification of land constraints at levels of unprecedented detail for the coast of NSW.

Dust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observations

The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

Soil erosion modelling for NSW coastal catchments using RUSLE in a GIS environment

In this study, hillslope erosion risk has been estimated for all eastern New South Wales (NSW) catchments, Australia using Revised Universal Soil Loss Equation (RUSLE) in a geographic information system (GIS) environment. Rainfall-runoff erosivity (R) factor was interpolated from NSW rainfall-erosivity contour (isoerodent) data. Soil erodibility (K) factor was based on the soil regolith stability and sediment yield classification. The classification was derived from soil landscape and related soil map data. The slope length and steepness (LS) factor was derived from high resolution digital elevation model (DEM). A fully-automated program using Arc Macro Language (AML) produced RUSLE-based LS factor grids for all coastal catchments. The outputs are comparable to the range of LS values summarised in the literature. Cover and management (C) factor and conservation support-practices (P) factor were set to one. They are intended to be allocated according to land use, ground cover and erosion control provisions for particular land management actions. The resulting erosion risk map, with pixel size of 25-m, provides unprecedented resolution of relative expected sheet and rill erosion across all NSW costal catchments and can be adapted for a range of erosion control purposes such as bushfire hazard reduction and comprehensive costal assessment.

Digital mapping of soil erodibility for water erosion in New South Wales, Australia

Soil erodibility represents the soil’s response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100 cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha–1 MJ–1 mm–1 with a mean (± s.d.) of 0.035 ± 0.007 t ha h ha–1 MJ–1 mm–1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.

Soil and land constraint assessment for urban and regional planning

Abstract A new approach to land capability assessment, termed soil and land constraint assessment, has been developed and carried out over an area of 60,000 km2 along the NSW coast and in the Hawkesbury-Nepean Catchment in eastern NSW. The system allows for the generation of soil and land constraint maps that portray in detail the physical potential of the land to support a range of land uses, including standard residential development, agriculture and domestic wastewater disposal. It involves a semi-quantitative analysis of soil-landscape constraints such as erosion, flood and mass movement hazards, and includes a broad indication of potential costs necessary to overcome these constraints. The system has been extensively field tested and was found to be reliable in approximately 80% of sites, with a tendency to give conservative results (i.e. indicate higher constraints than actually present). It is recommended for broadscale planning only and not site-specific development control. Twenty of the 34 standard land use zones used in Local Environmental Plans in NSW are effectively covered. The results can be readily interpreted by land use planners and land managers and should contribute to environmentally sustainable land use decision making. The process could be adapted to apply to similar natural resource datasets in other Australian states.

Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Angstrom exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

Dust storm detection for Xingjiang region using Indian National Satellite (INSAT 3A) data

Taklimakan Desert, located in southwest Xinjiang Uyghur Autonomous Region, is one of the predominant dust origin in China. Dust is one of the main types of atmospheric aerosol in this region. Emerging remote sensing imagery from geostationary meteorological satellite undeniably becomes an ideal mean for monitoring large regional distribution and intensity of dust storms. Among them, Indian National Satellite (INSAT 3A) is suitable for dust aerosol retrieval and dust storm detection for Xinjiang region as it can provide high spatiotemporal earth observation with a Charge Couple Device (CCD) camera. The camera contains three bands with a spatial resolution of 1km, which are very applicable for AOD retrieval. However, there is still no mature algorithm for the retrieval of AOD over land using INSAT 3A data, though some work have been done with other geostationary satellites [1-4].

Radar-based rainfall erosivity and hillslope erosion modelling in a burnt national park after storm events

Post-bushfire hillslope erosion is a major threat to soil health, water quality and ecosystem function. It can cause catastrophic impacts if followed by heavy storm events. Quantitative and timely assessment of hillslope erosion after bushfires during individual storm events is essential but remain a research challenge. We aimed to develop validated methodology to predict hillslope erosion in near real-time for fire-affected national parks and to evaluate the impact of bushfires on sediment delivery downstream. In this case study, we estimated rainfall erosivity and hillslope erosion based on weather radar images at 10-min intervals and produced time-series maps showing spatial distribution of rainfall erosivity and soil loss during storm events after a severe bushfire in early 2013 in Warrumbungle National Park in Australia. This would allow decision makers to assess the extent and magnitude of storm impacts on bushfire affected areas and to design appropriate remedial activities.