@article{jiang_yu_mickler_tang_liang_zhang_song_wang_2023, title={Forest Phenology under Differing Topographic Conditions: A Case Study of Changbai Mountain in Northeast China}, volume={14}, ISSN={["1999-4907"]}, DOI={10.3390/f14071466}, abstractNote={Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R2 = 0.93, p < 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R2 = 0.90, p < 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R2 = 0.53, p = 0.04), EOS was delayed by 0.18 d (R2 = 0.82, p = 0.002), and the LOS increased by 0.28 d (R2 = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.}, number={7}, journal={FORESTS}, author={Jiang, Jie and Yu, Quanzhou and Mickler, Robert A. A. and Tang, Qingxin and Liang, Tianquan and Zhang, Hongli and Song, Kaishan and Wang, Shaoqiang}, year={2023}, month={Jul} }
 @article{yu_mickler_liang_liu_jiang_song_wang_2022, title={Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China}, volume={13}, ISSN={["2150-7058"]}, DOI={10.1080/2150704X.2022.2088255}, number={8}, journal={REMOTE SENSING LETTERS}, author={Yu, Quanzhou and Mickler, Robert A. and Liang, Tianquan and Liu, Yujie and Jiang, Jie and Song, Kaishan and Wang, Shaoqiang}, year={2022}, month={Aug}, pages={800–811} }
 @article{mickler_2021, title={Carbon emissions from a temperate coastal peatland wildfire: contributions from natural plant communities and organic soils}, volume={16}, ISSN={["1750-0680"]}, DOI={10.1186/s13021-021-00189-0}, abstractNote={Abstract Background One of the scientific challenges of understanding climate change has been determining the important drivers and metrics of global carbon (C) emissions and C cycling in tropical, subtropical, boreal, subarctic, and temperate peatlands. Peatlands account for 3% of global land cover, yet contain a major reservoir of 550 gigatons (Gt) of soil C, and serve as C sinks for 0.37 Gt of carbon dioxide (CO 2 ) a year. In the United States, temperate peatlands are estimated to store 455 petagrams of C (PgC). There has been increasing interest in the role of wildfires in C cycling and altering peatlands from C sinks to major C sources. We estimated above- and below-ground C emissions from the Pains Bay Fire, a long-duration wildfire (112 days; 18,329 ha) that burned a coastal peatland in eastern North Carolina, USA. Results Soil C emissions were estimated from pre- and post-burn Light Detection and Ranging (LIDAR) soil elevation data, soils series and C content mapping, remotely sensed soil burn severity, and post-burn field surveys of soil elevation. Total above-ground C emissions from the fire were 2,89,579 t C and 214 t C ha −1 for the 10 vegetation associations within the burn area perimeter. Above-ground sources of C emissions were comprised of litter (69,656 t C), shrub (1,68,983 t C), and foliage (50,940 t C). Total mean below-ground C emissions were 5,237,521 t C, and ranged from 2,630,529 to 8,287,900 t C, depending on organic matter content of different soil horizons within each of the 7 soil series. The mean below-ground C emissions within the burn area were 1,595.6 t C ha −1 and ranged from 629.3 to 2511.3 t C ha −1 . Conclusions In contrast to undisturbed temperate peatlands, human induced disturbances of the natural elevation gradient of the peatland has resulted in increased heterogeneity of floristic variation and assemblages that are a product of the spatial and temporal patterns of the water table level and the surface wetness across peatlands. Human induced changes in surface hydrology and land use influenced the fuel characteristics of natural vegetation and associated soils, thus influencing wildfire risk, behavior, and the resulting C emissions.}, number={1}, journal={CARBON BALANCE AND MANAGEMENT}, author={Mickler, Robert A.}, year={2021}, month={Sep} }
 @article{wang_li_ju_chen_chen_croft_mickler_yang_2020, title={Estimation of Leaf Photosynthetic Capacity From Leaf Chlorophyll Content and Leaf Age in a Subtropical Evergreen Coniferous Plantation}, volume={125}, ISSN={["2169-8961"]}, DOI={10.1029/2019JG005020}, abstractNote={Photosynthetic rate is a key source of uncertainty in the modeling of the terrestrial carbon cycle. Recent studies have utilized leaf chlorophyll content (Chl) as a proxy for leaf photosynthetic capacity in croplands and deciduous forests, with little investigation into this relationship for other plant function types and for different leaf ages. In this study, we evaluated the relationship between the maximum rate of carboxylation (Vcmax25) and the maximum electron transport capacity (Jcmax25) at 25 °C with both leaf nitrogen and Chl from different leaf ages (current and previous year) in Masson's pine (Pinus massoniana Lamb.) and slash pine (Pinus elliottii Engelm.) species in a subtropical evergreen coniferous forest. Our results showed small changes in leaf nitrogen over the growing season. In contrast, Vcmax25, Jmax25, and Chl displayed larger seasonal variations. Vcmax25 was more related to leaf Chl than leaf nitrogen in both previous year's and current year's leaves, likely due to the variable partitioning of leaf nitrogen between and within photosynthetic and nonphotosynthetic fractions. Leaf Chl and month after budding (MAB) were the main predictors for Vcmax25 based on the random forest regression analysis. These findings highlighted the problem in using leaf nitrogen as a proxy for Vcmax25 where there is a dynamic nitrogen investment (i.e., with leaf ontogenesis, or between different species) and illustrated the value of using leaf Chl (as retrievable from remotely sensing) and MAB to constrain Vcmax25 in process-based models to improve the simulation of photosynthetic rates in evergreen coniferous forests.}, number={2}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES}, author={Wang, Shaoqiang and Li, Yue and Ju, Weimin and Chen, Bin and Chen, Jinghua and Croft, Holly and Mickler, Robert A. and Yang, Fengting}, year={2020}, month={Feb} }
 @article{chen_zhang_chen_zhang_ma_li_zhang_wu_wang_mickler_2020, title={Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize}, volume={12}, ISSN={["2072-4292"]}, DOI={10.3390/rs12172812}, abstractNote={The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance > 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.}, number={17}, journal={REMOTE SENSING}, author={Chen, Jinghua and Zhang, Qian and Chen, Bin and Zhang, Yongguang and Ma, Li and Li, Zhaohui and Zhang, Xiaokang and Wu, Yunfei and Wang, Shaoqiang and Mickler, Robert A.}, year={2020}, month={Sep} }
 @article{yu_mickler_liu_sun_zhou_zhang_deng_liang_2020, title={Remote Sensing of Potamogeton crispus L. in Dongping Lake in the North China Plain Based on Vegetation Phenology}, volume={48}, ISSN={["0974-3006"]}, DOI={10.1007/s12524-020-01103-w}, number={4}, journal={JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING}, author={Yu, Quanzhou and Mickler, Robert A. and Liu, Yujie and Sun, Leigang and Zhou, Lei and Zhang, Baohua and Deng, Huanguang and Liang, Lili}, year={2020}, month={Apr}, pages={563–573} }
 @article{wang_wang_wang_yan_mickler_shi_he_huang_zhou_2018, title={Detection of Positive Gross Primary Production Extremes in Terrestrial Ecosystems of China During 1982-2015 and Analysis of Climate Contribution}, volume={123}, ISSN={["2169-8961"]}, DOI={10.1029/2018JG004489}, abstractNote={Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems. High-temperature extremes and associated droughts significantly reduce terrestrial ecosystem carbon uptake, but there is uncertainty as to whether climatic extremes can be beneficial to ecosystem carbon uptake. In this study, we used three ecological models: the Boreal Ecosystem Productivity Simulator, the Terrestrial Ecosystem Carbon flux model, and the Global Production Efficiency Model coupled with the Carbon Exchange between Vegetation, Soil, and the Atmosphere model, to simulate China's terrestrial ecosystem GPP during the study period of 1982–2015. Positive GPP extremes were identified on yearly scales and analyzed for their temperature, precipitation, and solar radiation attributions. We found that the 3 years of positive GPP extremes occurred in 1990, 1998, and 2013, with the detrended GPP anomalies of 0.4 PgC/year, 0.2 PgC/year, and 0.3 PgC/year, respectively. Maximum GPP years were associated with an increased carbon uptake in response to increasing temperature and solar radiation under adequate precipitation for plant growth. China's subtropical-tropical monsoonal region, a region dominated by managed forests and agricultural lands, contributed the largest in GPP extremes and accounted for 46%, 50%, and 46% of the total detrended GPP anomalies in 1990, 1998, and 2013, respectively. Positive GPP extremes were associated with increasing temperature and solar radiation, indicating favorable positive climate extremes would be beneficial to carbon uptake in China's terrestrial ecosystem. This study provides a method to assess positive GPP extremes in response to global climate change.}, number={9}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES}, author={Wang, Miaomiao and Wang, Shaoqiang and Wang, Junbang and Yan, Hao and Mickler, Robert A. and Shi, Hao and He, Honglin and Huang, Mei and Zhou, Lei}, year={2018}, month={Sep}, pages={2807–2823} }
 @article{mickler_welch_bailey_2017, title={CARBON EMISSIONS DURING WILDLAND FIRE ON A NORTH AMERICAN TEMPERATE PEATLAND}, volume={13}, ISSN={["1933-9747"]}, DOI={10.4996/fireecology.1301034}, abstractNote={Northern temperate zone (30° to 50° latitude) peatlands store a large proportion of the world’s terrestrial carbon (C) and are subject to high-intensity, stand-replacing wildfires characterized by flaming stage combustion of aboveground vegetation and long-duration smoldering stage combustion of organic soils. Coastal peatlands are a unique region in which long-duration wildfire soil combustion is responsible for the majority of total annual emissions from all wildfires in the North American coastal plain. We developed a new method and approach to estimate aboveground and belowground C emissions from a 2008 peatland wildfire by analyzing vegetation C losses from field surveys of biomass consumption from the fire and soil C losses derived from the Soil Survey Geographic Database, a digital elevation model derived from airborne optical remote-sensing technology and ground elevation surveys using a Global Navigation Satellite System receiver. The approach to estimate belowground C emissions employed pre-fire LI-DAR-derived elevation from ground return points coupled with post-fire survey-grade GPS elevation measurements from co-located ground return points. Aboveground C emission calculations were characterized for litter, shrub foliage and woody biomass, and tree foliage fractions in different vegetation classes, thereby providing detailed emissions sources. The estimate of wildland fire C emissions considered the contribution of hydrologic regime and land management to fire severity and peat burn depth. The peatland wildfire had a mean peat burn depth of 0.42 m and resulted in estimated belowground fire emissions of 9.16 Tg C and aboveground fire emissions of 0.31 Tg C, for total fire emissions of 9.47 Tg C (1 Tg = 1012 grams). The mean belowground C emissions were estimated at 544.43 t C ha−1, and the mean aboveground C emissions were 18.33 t C ha−1 (1 t = 106 grams).}, number={1}, journal={FIRE ECOLOGY}, author={Mickler, Robert A. and Welch, David P. and Bailey, Andrew D.}, year={2017}, pages={34–57} }
 @article{yu_wang_mickler_huang_zhou_yan_chen_han_2014, title={Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China}, volume={6}, ISSN={["2072-4292"]}, DOI={10.3390/rs6098986}, abstractNote={Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI[705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination with GPP and Re (R2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China’s temperate broad-leaved Korean pine forest ecosystems.}, number={9}, journal={REMOTE SENSING}, author={Yu, Quanzhou and Wang, Shaoqiang and Mickler, Robert A. and Huang, Kun and Zhou, Lei and Yan, Huimin and Chen, Diecong and Han, Shijie}, year={2014}, month={Sep}, pages={8986–9013} }
 @article{mickler_earnhardt_moore_2002, title={Modeling and spatially distributing forest net primary production at the regional scale}, volume={52}, ISSN={["2162-2906"]}, DOI={10.1080/10473289.2002.10470793}, abstractNote={Forest, agricultural, rangeland, wetland, and urban landscapes have different rates of carbon sequestration and total carbon sequestration potential under alternative management options. Changes in the proportion and spatial distribution of land use could enhance or degrade that area's ability to sequester carbon in terrestrial ecosystems. As the ecosystems within a landscape change due to natural or anthropogenic processes, they may go from being a carbon sink to a carbon source or vice versa. Satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of terrestrial carbon. The coupling of Landsat Thematic Mapper (TM) data with a physiologically based forest productivity model (PnET-II) and historic climatic data provides an opportunity to enhance field plot-based forest inventory and monitoring methodologies. We use periodic forest inventory data from the U.S. Department of Agriculture (USDA) Forest Service's Forest Inventory and Analysis (FIA) Program to obtain estimates of forest area and type and to generate estimates of carbon storage for evergreen, deciduous, and mixed-forest classes. The area information is used in an accuracy assessment of remotely sensed forest cover at the regional scale. The map display of modeled net primary production (NPP) shows a range of forest carbon storage potentials and their spatial relationship to other landscape features across the southern United States. This methodology addresses the potential for measuring and projecting forest carbon sequestration in the terrestrial biosphere of the southern United States.}, number={4}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Mickler, RA and Earnhardt, TS and Moore, JA}, year={2002}, month={Apr}, pages={407–415} }
 @article{mickler_earnhardt_moore_2002, title={Regional estimation of current and future forest biomass}, volume={116}, ISSN={["1873-6424"]}, DOI={10.1016/S0269-7491(01)00241-X}, abstractNote={The 90,674 wildland fires that burned 2.9 million ha at an estimated suppression cost of $1.6 billion in the United States during the 2000 fire season demonstrated that forest fuel loading has become a hazard to life, property, and ecosystem health as a result of past fire exclusion policies and practices. The fire regime at any given location in these regions is a result of complex interactions between forest biomass, topography, ignitions, and weather. Forest structure and biomass are important aspects in determining current and future fire regimes. Efforts to quantify live and dead forest biomass at the local to regional scale has been hindered by the uncertainty surrounding the measurement and modeling of forest ecosystem processes and fluxes. The interaction of elevated CO2 with climate, soil nutrients, and other forest management factors that affect forest growth and fuel loading will play a major role in determining future forest stand growth and the distribution of species across the southern United States. The use of satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of forest carbon. The incorporation of Landsat Thematic Mapper data coupled with a physiologically based productivity model (PnET), soil water holding capacity, and historic and projected climatic data provides an opportunity to enhance field plot based forest inventory and monitoring methodologies. We use periodic forest inventory data from the USDA Forest Service's Forest Inventory and Analysis (FIA) project to obtain estimates of forest area and type to generate estimates of carbon storage for evergreen, deciduous, and mixed forest classes for use in an assessment of remotely sensed forest cover at the regional scale for the southern United States. The displays of net primary productivity (NPP) generated from the PnET model show areas of high and low forest carbon storage potential and their spatial relationship to other landscape features for the southern United States. At the regional scale, predicted annual NPP in 1992 ranged from 836 to 2181 g/m2/year for evergreen forests and 769-2634 g/m2/year for deciduous forests with a regional mean for all forest land of 1448 g/m2/year. Prediction of annual NPP in 2050 ranged from 913 to 2076 g/m2/year for evergreen forest types to 1214-2376 g/m2/year for deciduous forest types with a regional mean for all forest land of 1659 g/m2/year. The changes in forest productivity from 1992 to 2050 are shown to display potential areas of increased or decreased forest biomass. This methodology addresses the need for spatially quantifying forest carbon in the terrestrial biosphere to assess forest productivity and wildland fire fuels.}, journal={ENVIRONMENTAL POLLUTION}, author={Mickler, RA and Earnhardt, TS and Moore, JA}, year={2002}, pages={S7–S16} }