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    Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery
    (MDPI, 2017-06-29) Dempewolf, Jan; Nagol, Jyoteshwar; Hein, Sebastian; Thiel, Carsten; Zimmerman, Reiner
    Tree height growth measurements at monthly and annual time scales are important for calibrating and validating forest growth models, forest management and studies of forest ecology and biophysical processes. Previous studies measured the terminal growth of individual trees or forest stands at annual or decadal time scales. Short-term, within-season measurements, however, are largely unavailable due to technical and practical limitations. Here, we describe a novel approach for measuring within-season tree height growth using a time series of co-registered digital surface models obtained with a low-cost unmanned aerial vehicle in combination with ground control plates and Structure from Motion data processing. The test site was a 2-hectare temperate mixed forest stand of varying age and successional stage in central Europe. Our results show median growth rates between 27 May and 19 August of 68 cm for Norway spruce, 93 cm for Scots pine, 106 cm for Silver birch and 26 cm for European beech. The results agree well with published field observations for these species. This study demonstrates the capability of inexpensive, increasingly user-friendly and versatile UAV systems for measuring tree height growth at short time scales, which was not previously possible, opening up new avenues for investigation and practical applications in forestry and research.
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    Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
    (MDPI, 2017-08-07) DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.
    We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.
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    Expansion of Industrial Plantations Continues to Threaten Malayan Tiger Habitat
    (MDPI, 2017-07-19) Shevade, Varada S.; Potapov, Peter V.; Harris, Nancy L.; Loboda, Tatiana V.
    Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we estimate the natural forest loss and conversion to plantations in Peninsular Malaysia and specifically in its tiger habitat between 1988 and 2012 using the Landsat data archive. We estimate a total loss of 1.35 Mha of natural forest area within Peninsular Malaysia over the entire study period, with 0.83 Mha lost within the tiger habitat. Nearly half (48%) of the natural forest loss area represents conversion to tree plantations. The annual area of new plantation establishment from natural forest conversion increased from 20 thousand ha year−1 during 1988–2000 to 34 thousand ha year−1 during 2001–2012. Large-scale industrial plantations, primarily those of oil palm, as well as recently cleared land, constitute 80% of forest converted to plantations since 1988. We conclude that industrial plantation expansion has been a persistent threat to natural forests within the Malayan tiger habitat. Expanding oil palm plantations dominate forest conversions while those for rubber are an emerging threat.
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    Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval
    (MDPI, 2017-11-04) Che, Xianghong; Feng, Min; Sexton, Joseph O.; Channan, Saurabh; Yang, Yaping; Sun, Qing
    Measurements of solar radiation reflected from Earth’s surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the “bi-directional” geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R2 (0.63–0.955), regression slopes closer to unity (0.718–0.955), lower root mean squared difference (RMSD) (0.422–3.142), and lower mean absolute error (MAE) (0.282–1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.
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    Uncovering the Green, Blue, and Grey Water Footprint and Virtual Water of Biofuel Production in Brazil: A Nexus Perspective
    (MDPI, 2017-11-08) Munoz Castillo, Raul; Feng, Kuishuang; Hubacek, Klaus; Sun, Laixiang; Guilhoto, Joaquim; Miralles-Wilhelm, Fernando
    Brazil plays a major role in the global biofuel economy as the world’s second largest producer and consumer and the largest exporter of ethanol. Its demand is expected to significantly increase in coming years, largely driven by national and international carbon mitigation targets. However, biofuel crops require significant amounts of water and land resources that could otherwise be used for the production of food, urban water supply, or energy generation. Given Brazil’s uneven spatial distribution of water resources among regions, a potential expansion of ethanol production will need to take into account regional or local water availability, as an increased water demand for irrigation would put further pressure on already water-scarce regions and compete with other users. By applying an environmentally extended multiregional input-output (MRIO) approach, we uncover the scarce water footprint and the interregional virtual water flows associated with sugarcane-derived biofuel production driven by domestic final consumption and international exports in 27 states in Brazil. Our results show that bio-ethanol is responsible for about one third of the total sugarcane water footprint besides sugar and other processed food production. We found that richer states such as São Paulo benefit by accruing a higher share of economic value added from exporting ethanol as part of global value chains while increasing water stress in poorer states through interregional trade. We also found that, in comparison with other crops, sugarcane has a comparative advantage when rainfed while showing a comparative disadvantage as an irrigated crop; a tradeoff to be considered when planning irrigation infrastructure and bioethanol production expansion.