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

ABSTRACT

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.html     

Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and the ECMWF model

GPS Solutions - We characterize the spatial–temporal variability of integrated water vapor (IWV) in Ethiopia from a network of global positioning system (GPS) stations and the European Center...     

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.     

Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco

Abstract

The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats.     

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal,South Africa

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.     

Monitoring Horizontal and Vertical Cropping Pattern and Dynamics in Bihar over a Decade (2001–2012) Based on Time-Series Satellite Data

Increasing population and natural disasters like drought, flood, cyclone etc., has impacted global agriculture area and hence continuously modifying cropping pattern and associated statistics. The present study analysed agriculture dynamics over one of the densely populated and disaster prone state (Bihar) in India and derived vital statistics (single, double and triple cropping area, and monthly, seasonal, annual and long term status at the state and district level) for the years 2001–2012. The study used time-series MODIS vegetation index (EVI; MOD13A2, 1 km, 16 day, 2001–2012), MODIS annual Land Cover product (MCD12Q1, 500 m, 2001–2012) and Global Land Cover map (Scasia_V4, 1 km, 2000; Globcover_V2.2, 300 m, 2005/2006 and V2.3, 2009, 300 m), and extracted horizontal (i.e., area change) and vertical (i.e., cropping intensification) agriculture change pattern. The results were inter-compared, and validated using government reports as well as with high spatial resolution data (IRS-LISS III 23.5 m). From 2001–2006 to 2007–2012, the net horizontal and vertical change in agriculture area is +145.24 and +907.82 km², respectively, and net change in seasonal crop area (winter, summer and monsoon) is +959.21, +1009.84 and ?1061.64 km², respectively. The districts which are located along the eastern part of Ganges experienced maximum positive changes and the districts along Gandak river in the north-western part of the study area experienced maximum negative changes. Overall, the study has quantified and revealed interesting space–time agriculture change patterns over 12 years including impacts caused by droughts and floods in the study area.     

Variations in annual snowline and area of an ice-covered stratovolcano in the Cordillera Ampato,Peru, using remote sensing data (1986–2014)

This research focuses on the recent variations in the annual snowline and the total glaciated area of the Nevado Coropuna in the Cordillera Ampato, Peru. Maximum snowline altitude towards the end of dry season is taken as a representative of the equilibrium line altitude of the year, which is an indirect measurement of the annual mass balance. We used Landsat and IRS LISS3 images during the last 30 years due to its better temporal coverage of the study site. It is found that there was a decrease of 26.92% of the glaciated area during 1986–2014. We calculated the anomalies in precipitation and temperature in this region and also tried to correlate the changes in glacier parameters with the combined influence of El Niño – Southern Oscillation (ENSO) and pacific decadal oscillation (PDO). It is concluded that the snowline of Nevado Coropuna has been fluctuated during ENSO, and maximum fluctuations were observed when ENSO and PDO were in phase.     

Monitoring thickness and volume changes of the Dongkemadi Ice Field on the Qinghai-Tibetan Plateau (1969–2000) using Shuttle Radar Topography Mission and map data

Abstract

This paper presents the first measurement of multi-decadal thickness and volume changes (1969–2000) of the Dongkemadi Ice Field (DIF) in the Tanggula Mountains, central Qinghai-Tibetan Plateau, China, using multi-source remote sensing data. These include the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) acquired in February, 2000, a DEM generated by digitising analogue topographic maps from 1969, and Landsat ETM+ imagery from 2000. Digital glacier outlines and GIS-based processing were used to calculate an elevation difference map to evaluate the relative elevation error of these two DEMs over ice-free areas. This method was also used to identify regions of glacier elevation thinning and thickening corresponding to glacier mass loss and gain. Analysis of 67,520 points on flat grass and rock terrain surrounding the DIF, with a slope less than 25°, showed a mean elevation difference of –0.90 m and a standard deviation of 5.58 m. A thickness change error within ±6 m was estimated. Between 1969 and 2000, 76.51% of the whole DIF area appeared to be thinning while 23.49% showed thickening. The average glacier surface thinning was –12.58 m with a standard deviation of 18.29 m and the estimated volume loss was 1.17 km³. The standard deviation of volume change was 0.0006 km³ over the DIF. A thinning rate up to 0.41±0.194 m a^?1 or 0.038 km³ a^?1 for the volume loss was observed for the whole ice field, which seems to be evidence for the ongoing retreat of glaciers on the Qinghai-Tibetan Plateau. It was found that the spatial thickness change pattern derived from the remote sensing method was consistent with the thickness change results of the Small Dongkemadi Glacier (SDG) from field measurements. The estimated error of the annual thickness change rate was on the order of 5%. The relationship between elevation change and absolute glacier elevation over typical glaciers was also analysed, showing considerable variability. These changes have possibly resulted from increased temperature and decreased precipitation in this region.