Geography

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Author: search.filters.author.Pickens, Amy

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    Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan
    (MDPI, 2018-03-21) Khan, Ahmad; Hansen, Matthew C.; Potapov, Peter V.; Adusei, Bernard; Pickens, Amy; Krylov, Alexander; Stehman, Stephen V.
    While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat/no wheat training data, we first converted the continuous training data to a wheat/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat/no wheat threshold to produce Map 3, and the second method used only pure wheat (≥75% cover) and no wheat (≤25% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (±standard error): 88% (±4%) for Map 1, 90% (±4%) for Map 2, 90% (±4%) for Map 3, and 87% (±4%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery.
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    Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping
    (MDPI, 2020-01-29) Potapov, Peter; Hansen, Matthew C.; Kommareddy, Indrani; Kommareddy, Anil; Turubanova, Svetlana; Pickens, Amy; Adusei, Bernard; Tyukavina, Alexandra; Ying, Qing
    The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.
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    DYNAMICS OF GLOBAL SURFACE WATER 1999 - PRESENT
    (2021) Pickens, Amy; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Inland surface waters are critical to life, supplying fresh water and habitat, but are constantly in flux. There have been considerable advances in surface water monitoring over the last decade, though the extent of surface water has not been well-quantified per international reporting standards. Global characterizations of change have been primarily bi-temporal. This is problematic due to significant areas with multi-year cycles of wet and dry periods or anomalous high water or drought years. Many areas also exhibit strong seasonal fluctuations, such as floodplains and other natural wetlands. This dissertation aims to characterize open surface water extent dynamics by employing all of the Landsat archive 1999-present, and to report area estimates with associated uncertainty measures as required by policy guidelines. From 1999 to 2018, the extent of permanent water (in liquid or ice state) was 2.93 (standard error ±0.09) million km2, representing only 60.82 (±1.93)% of the total area that had water for some duration of the period. The unidirectional loss and gain areas were relatively small, accounting for only 1.10 (±0.23)% and 2.87 (±0.58)% of total water area, respectively. The area that transitioned multiple times between water and land states on an annual scale was over four times larger (19.74 (±2.16)%), totaling 0.95 (±0.10) million km2, establishing the need to evaluate the time-series from the entire period to assess change dynamics. From a seasonal perspective, June has over double the amount of open surface water as January, with 3.91 (±0.19) million km2 and 1.59 (±0.21) million km2, respectively. This is due to the vast network of lakes and rivers across the high-latitudes of the northern hemisphere that freeze over during the winter, with a maximum extent of ice over areas of permanent and seasonal water in February, totaling 2.49 (±0.25) million km2. This is the first global study to estimate the areas of extent and change with associated uncertainty measures and evaluate the seasonal dynamics of surface water and ice in a combined analysis. The methods developed here provide a framework for continuing to evaluate past trends and monitoring current dynamics of surface water and ice.