Enforcement Evasion Highlights Need for Better Satellite‐Based Forest Governance

Our recent article, “Are Brazil’s Deforesters Avoiding Detection?” demonstrated that focusing illegal deforestation enforcement on the subset of forest monitored by the flagship PRODES system has caused PRODES to capture a declining share of deforestation in the Brazilian Amazon. Deforesters may be purposively seeking out forests not monitored for enforcement. Addressing the problem would help Brazil maintain a cutting‐edge forest governance model worthy of transfer to other nations. Two commentaries questioned our decision to investigate solely PRODES and not additional government monitoring systems. We focused on PRODES because it is the most salient deforestation monitoring system. Other key deforestation monitoring systems are all either limited to the same monitoring footprint as PRODES, not used for enforcement, or are rarely used for measuring forest loss in the Brazilian Amazon. We do agree with the commentaries that Brazil’s new satellite monitoring protocol for greenhouse gas emissions estimation is critical progress of the type we were advocating in our original article.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138285/1/conl12379_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138285/2/conl12379.pd

Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman-Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that physically integrates Moderate Resolution Imaging Spectroradiometer (MODIS)-derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth-Wallace framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at thirteen core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of -4 %. These error statistics show higher accuracies than a widely-used SEB-based Surface Energy Balance System (SEBS) and PM-based MOD16 ET, which were found to overestimate (PBIAS = 28 %) and underestimate ET (PBIAS = -26 %), respectively. The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapour pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient points to its potential for near real time ET mapping from regional to continental scales.NASA Land-Cover Land-Use Change Grant (NNX17AH97G)NASA new investigator program award (NNX16AI19G)BIOTRANS (grant number, 00001145)CAOS-2 project grant (INTER/DFG/14/02)STEREOIII (INTER/STEREOIII/13/03/HiWET; CONTRACT NR SR/00/301)https://deepblue.lib.umich.edu/bitstream/2027.42/143157/1/hess-2017-535.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143157/4/hess-22-2311-2018.pdfDescription of hess-2017-535.pdf : SUPERSEDED: for historical purposes onl

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low ra (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP \u3c 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of ra

Amniotic Band Syndrome with CTEV and Meningocele: A Rare Case Report

Amniotic band syndrome (ABS) is a group of rare congenital abnormalities caused by wrapping of parts of the foetus by fibrous amniotic bands during intrauterine life. It can be seen in infants without any known genetic mutations. Band formation most frequently affects the distal segments, including the hand. Here, we report a case of a neonate who presented with multiple congenital abnormalities and clinical features that suggest the Amniotic Band Syndrome. It was delivered by a 17-year-old female patient at 28 weeks period of gestation, who had a medical abortion

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research

Insights into the aerodynamic versus radiometric surface temperature debate in thermal-based evaporation modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research

Recent Advances in Remote Sensing of Evapotranspiration

Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products

A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models

In typical surface energy balance (SEB) models such as surface energy balance for land (SEBAL) and mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC), calibration of sensible heat flux (H) requires identification of endmember (i.e. hot and cold) pixels. Such pixel selection is typically done manually, which makes it labor intensive to apply SEBAL or METRIC over large spatial areas or long time series. In this paper, we introduce a new automated approach that uses an exhaustive search algorithm (ESA) to identify endmember pixels for use in these models. The fully automated models were applied on 134 near cloud-free Landsat images with each image covering one of four flux measurement sites covering a distinct land cover type in humid Florida or relatively drier Oklahoma. Observed land surface temperatures (T-s) at automatically identified hot and cold pixels were within 0.25% of manually identified pixels (both coefficient of determination, R-2, and Nash-Sutcliffe efficiency, NSE, >= 0.98, and root mean squared error, RMSE, <= 1.31 K). The new fully automated model performed better and demonstrated better consistency than an existing semi-automated method that used a statistical approach to subset coldest and hottest pixels within an image. Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE >= 0.94 and RMSE <= 0.35 mm day(-1)). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed automated pixel selection approach greatly reduces time demands (e.g. approximately one image per hour vs. hundreds of image per hour) for applying SEBAL and METRIC and allows for their more widespread and frequent use. This automation can also reduce potential bias introduced by an inexperienced operator and extends the domain of the models to new users.clos

Spectral analysis of Scotch pine infested by Sirex noctilio

Remote sensing methods for monitoring forest health have advanced with the development of sophisticated new tools and techniques in recent years. Prior research had explored needle-level analysis of Sirex infestation and this research explores a new method of investigating the impact of Sirex woodwasp (Sirex noctilio) infestation in Scotch pines using 8-band multispectral WorldView-2 imagery. The goal of the project was to assess if the broadband spectral regions associated with this sensor can characterize subtle changes in spectral reflectance caused by Sirex infestation. Eight different spectral indices were derived from the images and statistical analysis was performed. While the needle-level analysis previously reported showed statistical differences, none of the eight spectral indices showed statistically significant differences between the healthy and infested trees at a 0.05 significance level, though some showed differences with a weaker significance level. An automated calibration model based on exhaustive search techniques was used to consider the optimum threshold to determine the capability of the vegetation index (ices) to detect changes in spectral reflectance from the infested trees. The accuracy assessment results were poor to moderate. The best overall accuracy was 68% with a Kappa coefficient of 0.33. While there is an opportunity to explore multispectral spectral bands of WorldView-2 data in mapping forest health using spectral indices, in our study, the broadband spectral indices were not capable of accurately differentiating the subtle changes in spectral properties of healthy and infested tree at the individual tree level

A Critical Evaluation on the Role of Aerodynamic and Canopy–Surface Conductance Parameterization in SEB and SVAT Models for Simulating Evapotranspiration: A Case Study in the Upper Biebrza National Park Wetland in Poland

Evapotranspiration (ET) estimation through the surface energy balance (SEB) and soil-vegetation-atmosphere-transfer (SVAT) models are uncertain due to the empirical parameterizations of the aerodynamic and canopy-substrate conductances (gA and gS) for heat and water vapor transfers. This study critically assessed the impact of conductance parameterizations on ET simulation using three structurally different SEB and SVAT models for an ecologically important North-Eastern European wetland, Upper Biebrza National Park (UBNP) in two consecutive years 2015 and 2016. A pronounced ET underestimation (mean bias −0.48 to −0.68 mm day−1) in SEBS (Surface Energy Balance System) was associated with an overestimation of gA due to uncertain parameterization of momentum roughness length and bare soil’s excess resistance to heat transfer (kB−1) under low vegetation cover. The systematic ET overestimation (0.65⁻0.80 mm day−1) in SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was attributed to the overestimation of both the conductances. Conductance parameterizations in SEBS and SCOPE appeared to be very sensitive to the general ecohydrological conditions, with a tendency of overestimating gA (gS) under humid (arid) conditions. Low ET bias in the analytical STIC (Surface Temperature Initiated Closure) model as compared to SEBS/SCOPE indicated the critical need for calibration-free conductance parameterizations for improved ET estimation