Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

BNL-113514-2017-JA Long-term Post-disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed using Landsat Time Series Stack Feng R. Zhao, Ran Meng, Chengquan Huang, Maosheng Zhao, Feng A. Zhao, Peng Gong, Le Yu, and Zhiliang Zhu Accepted for publication in Remote Sensing June 2016 Environmental & Climate Science Dept. Brookhaven National Laboratory U.S. Department of Energy USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23) Notice: This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. 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The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. 1 of 25 1 Article 2 Long-term Post-disturbance Forest Recovery in the 3 Greater Yellowstone Ecosystem Analyzed using Landsat 4 Time Series Stack 5 Feng R. Zhao 1, *, Ran Meng 2 , Chengquan Huang 1, Maosheng Zhao 1, Feng A. Zhao 1, Peng Gong 3, Le 6 Yu 3, and Zhiliang Zhu 4 7 1 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA, E-Mails: 8 [email protected] (F.R.Z.) ; [email protected] (C.H.) ; [email protected] (M.Z.) ; [email protected] (F.A.Z.) 9 2 Environmental and Climate Sciences Department, Brookhaven National Laboratory, Bldg. 490A, Upton, NY 10 11973, USA, E-Mail: [email protected] (R.M.) 11 3 Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua 12 University, Haidian, Beijing 10083, China, E-Mails : [email protected] (P.G.) ; [email protected] 13 (L.Y.) 14 4 U.S. Geological Survey, Reston, VA 20192, USA, E-Mail: [email protected] (Z.Z.) 15 * Correspondence: [email protected]; Tel.: +01-240-485-6468 16 Academic Editor: name 17 Received: date; Accepted: date; Published: date 18 Abstract: Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and 19 remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. 20 Although the disturbance products (tracking the conversion from forest to non-forest type) derived 21 using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been 22 validated extensively for mapping forest disturbances across the United States, the ability of this 23 approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest 24 type) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term 25 (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and 26 harvests) in the Greater Yellowstone Ecosystems (GYE). Using high spatial resolution images from 27 Google Earth, we validated the detectable forest spectral recovery status mapped by VCT by year 2011. 28 Validation results show that the VCT was able to map long-term post-disturbance forest spectral 29 recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. 30 Harvested areas in the GYE have higher percentages of forest spectral recovery than burned areas by 31 year 2011, and National Forests land generally has higher spectral recovery rates compared with 32 National Parks. With the availability of the VCT product nationwide, this approach can also be applied 33 to examine long-term post-disturbance forest spectral recovery in other study regions across the U.S. 34 Keywords: Wildland Fires; Timber Harvest; Detectable Forest Spectral Recovery; 1988 Yellowstone Fires 35 1. Introduction 36 Forests in the Greater Yellowstone Ecosystem (GYE) experience frequent natural (e.g., wildfires, 37 insect and disease outbreaks, snow and wind damage) and anthropogenic (land use changes and timber 38 harvesting) disturbance events [1,2]. Recovery from past disturbance is an integral process in the carbon 39 and nutrient cycles [3,4]. Inclusion of the forest recovery process following disturbance is critical to 40 calculating regional C fluxes and can better inform policy makers on both the importance and uncertainty 41 of disturbances in the regulation of the regional and global carbon cycle [5]. Many ecosystem models 2 of 25 42 assume post-disturbance forest recovery occurs immediately or homogenously across the landscape. This 43 modeling hypothesis, however, has not been supported by ground observations [6]. Therefore, there is 44 urgent need to efficiently examine forest recovery conditions at large scales. 45 Despite the urgent needs in quantifying post-disturbance forest recovery, a universal definition of 46 forest recovery is not available [7]. Terminologies such as “regeneration”, “regrowth”, and “recovery” are 47 often used, sometimes interchangeably, to represent the return of vegetation (including grass, shrub, and 48 forests) following disturbances [7-9]. In the context of forest recovery, it is necessary to clearly define 49 forest, and the recovery of forest. In this study, we defined forest following the definition of the Food and 50 Agriculture Organization (FAO) of the United Nations, i.e., greater than 10% tree cover (FAO 1998). 51 Forest recovery was then defined as the return of forest cover to more than 10% of the area. 52 This definition of forest has allowed remote sensing as a tool for examining forest disturbance and 53 recovery over large areas [10-14], especially for fires [15-18] and logging [19-22]. Many remote sensing- 54 based studies have made contributions to our understanding of vegetation spectral recovery following 55 disturbances [8,23]. In particular, a series of Landsat mission satellites have been imaging the Earth’s 56 surface since 1972, creating a time series of Landsat observations that is highly valuable for tracking land 57 change history for over four decades [24-26]. Although numerous change detection procedures have been 58 developed [27-32], with some having succeeded in mapping forest disturbances and disturbance types 59 over large areas [31,33-40], characterizing long-term forest spectral recovery following specific 60 disturbance types remains challenging. Depending on local environmental conditions and post- 61 disturbance management practices, it often takes years to decades for forests to recover its functions, 62 following a stand-clearing disturbance event [41,42]. It is valuable to determine at what point in time 63 during this process young trees have started to grow back and restore forest ecosystem services. 64 One of the algorithms, the Vegetation Change Tracker (VCT), was designed to both detect forest 65 disturbance and track post-disturbance spectral recovery [43] using annual or biannual Landsat time 66 series stacks (LTSSs) [44]. Although the disturbance products (tracking the conversion from forest to non- 67 forest type) derived using this LTSS-VCT approach have been validated extensively for mapping forest 68 disturbances across the United States [45-48], the ability of this approach to characterize post-disturbance 69 spectral recovery has yet to be assessed. Based on the FAO forest definition, we can approximate the 70 recovery of forest cover to more than 10% of the area with spectral values within pre-determined 71 thresholds. Notably, a spectral recovery is not synonymous with ecological definitions of forest recovery 72 [43]. Spectral recovery signal is also highly dependent on the choice of remote sensing index and the 73 recovery definition. In addition, recovery does not imply that the pixel is occupied by the original species. 74 In the GYE, for example, a post-fire Whitebark Pine forest might grow back as birch forest in the first few 75 decades after the fire [49]. 76 In the GYE, forest recovery trends vary from one forest type to another. This mainly has to do with 77 species-specific regrowth attributes. Lodgepole Pine (Pinus contorta) and Aspen (Populus tremuloides) 78 are fast-growing species that usually occupy lower elevations and may grow back in 5 - 20 years after a 79 stand-clearing disturbance [50,51]. Forests that grow at higher elevations, such as Whitebark Pine (Pinus 80 albicaulis), Engelmann Spruce (Picea engelmannii) and Subalpine Fir (Abies lasiocarpa), usually take 81 longer time to recover [50]. Forest recovery trends in the GYE also differ between various land ownership 82 and disturbance types. The post-harvest forest recovery in national forest land is generally faster than the 83 post-fire forest recovery in both national parks and national forests, likely due to the following reasons. 1) 84 Land productivity in national forests is generally higher than that of the national parks and wilderness 85 areas [52]. Most fires occur at high-elevation low-productivity sites with shorter growing seasons, 86 whereas harvested sites generally have longer growing seasons and higher site productivity.

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