Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.htm

Data from: Landscape-specific thresholds in the relationship between species richness and natural land cover

1. Thresholds in the relationship between species richness and natural land cover can inform landscape-level vegetation protection and restoration targets. However, landscapes differ considerably in composition and other environmental attributes. If the effect of natural land cover on species richness depends on (i.e. interacts with) these attributes, and this affects the value of thresholds in this relationship, such dependencies must be considered when using thresholds to guide landscape management. 2. We hypothesised that the amount of natural land cover at which a threshold occurs would differ in predictable ways with particular anthropogenic, abiotic and biotic attributes of landscapes. To test this, we related woodland bird species richness in 251 landscapes, each 100 km 2, to natural land cover in south-east Australia. We compared the fit of exponential and threshold models of the richness-natural land cover relationship, focussing on the extent of natural land cover at which thresholds presented among landscapes that differed in matrix land use intensity, heterogeneity, productivity and the prevalence of strong biotic interactors. We used linear mixed modelling to examine how interactions between natural land cover and the various landscape attributes affected the fit of models of species richness. 3. Threshold models of the richness-natural land cover relationship were always a better fit than exponential models. Threshold values did not vary consistently with specific landscape attributes, with the exception of landscapes that were classified by the prevalence of strong biotic interactors (hypercompetitive native birds of the genus Manorina). 4. Natural land cover had a more positive effect on species richness in landscapes when Manorina prevalence was higher. This positive interaction provided the biggest improvement in explanatory power of models of species richness. 5. Synthesis and applications. While we detected an interaction between Manorina prevalence and the area of natural land cover, generalities relating to the underlying nature of thresholds in the richness-natural land cover relationship remain elusive. Complex interactions, relating to various landscape attributes and associated ecological processes, likely underpin variation in threshold values. Until these complexities are better understood, the use of thresholds for informing landscape management and conservation target setting should be approached with caution