Exploring A Stable Aspen Niche Within Aspen-Conifer Forests of Utah

Quaking aspen (Populus tremuloides Michx.) is the most widespread broadleaf tree species of North America. Increasing evidence shows that aspen has diverging ecological roles across its range as both “seral” and “stable” aspen community types. This leads us to believe that the successional pathway of aspen may not always lead to a climax conifer sere, but may in some cases consist of persisting stands of pure aspen. This study is an attempt to understand the relationship of aspen community types to climatic, physical, and biophysical variables by modeling patterns of aspen and conifer distribution using remote sensing and GIS technology. Study methodologies and results were specifically designed to aid land managers in identifying extent and status of aspen populations as well as prioritizing aspen restoration projects. Four study sites were chosen in order to capture the geographic and climatic range of aspen. Photointerpretation of NAIP color infrared imagery and linear unmixing of Landsat Thematic Mapper imagery were used to classify dominant forest cover. A Kappa analysis indicates photointerpretation methods to be more accurate (Khat=92.07%, N=85) than linear unmixing (Khat=51.05%, N=85). At each plot, variables were calculated and derived from DAYMET data, digital elevation models, and soil surveys, then assessed for precision and ability to model aspen and conifer distributions. A generalized linear model and discriminant analysis were used to assess habitat overlap between aspen and conifer and to predict areas where “stable” aspen communities are likely to occur. Results do not provide definitive evidence for a “stable” aspen niche. However, the model indicates 60 to 90 cm of total annual precipitation and topographic positions receiving greater than 4,500 Wh m‐2 d‐1 of solar radiation have a higher potential for “stable” aspen communities. Model predictions were depicted spatially within GIS as probability of conifer encroachment. In addition, prediction‐conditioned fallout rates and receiver operating characteristic curves were used to partition the continuous model output. Categorical maps were then produced for each study site delineating potential “stable” and “seral” aspen community types using an overlay analysis with landcover maps of aspen‐conifer forests

Deep Image Prior for Disentangling Mixed Pixels

A mixed pixel in remotely sensed images measures the reflectance and emission from multiple target types (e.g., tree, grass, and building) from a certain area. Mixed pixels exist commonly in spaceborne hyper-/multi-spectral images due to sensor limitations, causing the signature ambiguity problem and impeding high-resolution remote sensing mapping. Disentangling mixed pixels into the underlying constituent components is a challenging ill-posed inverse problem, which requires efficient modeling of spatial prior information and other application-dependent prior knowledge concerning the mixed pixel generation process. The recent deep image prior (DIP) approach and other application-dependent prior information are integrated into a Bayesian framework in the research, which allows comprehensive usage of different prior knowledge. The research improves mixed pixel disentangling using the Bayesian DIP in three key applications: spectral unmixing (SU), subpixel mapping (SPM), and soil moisture product downscaling (SMD). The main contributions are summarized as follows. First, to improve the decomposition of mixed pixels into pure material spectra (i.e., endmembers) and their constituting fractions (i.e., abundances) in SU, a designed deep fully convolutional neural network (DCNN) and a new spectral mixture model (SMM) with heterogeneous noise are integrated into a Bayesian framework that is efficiently solved by a new iterative optimization algorithm. Second, to improve the decomposition of mixed pixels into class labels of subpixels in SPM, a dedicated DCNN architecture and a new discrete SMM are integrated into the Bayesian framework to allow the use of both spatial prior and the forward model. Third, to improve the decomposition of mixed pixels into soil moisture concentrations of subpixels in SMD, a new DIP architecture and a forward degradation model are integrated into the Bayesian framework that is solved by the stochastic gradient descent approach. These new Bayesian approaches improve the state-of-the-art in their respective applications (i.e., SU, SPM, and SMD), which can be potentially utilized for solving other ill-posed inverse problems where simultaneously modeling of the spatial prior and other prior knowledge is needed

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Combining hyperspectral UAV and mulitspectral FORMOSAT-2 imagery for precision agriculture applications

Precision agriculture requires detailed information regarding the crop status variability within a field. Remote sensing provides an efficient way to obtain such information through observing biophysical parameters, such as canopy nitrogen content, leaf coverage, and plant biomass. However, individual remote sensing sensors often fail to provide information which meets the spatial and temporal resolution required by precision agriculture. The purpose of this study is to investigate methods which can be used to combine imagery from various sensors in order to create a new dataset which comes closer to meeting these requirements. More specifically, this study combined multispectral satellite imagery (Formosat-2) and hyperspectral Unmanned Aerial Vehicle (UAV) imagery of a potato field in the Netherlands. The imagery from both platforms was combined in two ways. Firstly, data fusion methods brought the spatial resolution of the Formosat-2 imagery (8 m) down to the spatial resolution of the UAV imagery (1 m). Two data fusion methods were applied: an unmixing-based algorithm and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The unmixing-based method produced vegetation indices which were highly correlated to the measured LAI (rs= 0.866) and canopy chlorophyll values (rs=0.884), whereas the STARFM obtained lower correlations. Secondly, a Spectral-Temporal Reflectance Surface (STRS) was constructed to interpolate a daily 101 band reflectance spectra using both sources of imagery. A novel STRS method was presented, which utilizes Bayesian theory to obtain realistic spectra and accounts for sensor uncertainties. The resulting surface obtained a high correlation to LAI (rs=0.858) and canopy chlorophyll (rs=0.788) measurements at field level. The multi-sensor datasets were able to characterize significant differences of crop status due to differing nitrogen fertilization regimes from June to August. Meanwhile, the yield prediction models based purely on the vegetation indices extracted from the unmixing-based fusion dataset explained 52.7% of the yield variation, whereas the STRS dataset was able to explain 72.9% of the yield variability. The results of the current study indicate that the limitations of each individual sensor can be largely surpassed by combining multiple sources of imagery, which is beneficial for agricultural management. Further research could focus on the integration of data fusion and STRS techniques, and the inclusion of imagery from additional sensors.Samenvatting In een wereld waar toekomstige voedselzekerheid bedreigd wordt, biedt precisielandbouw een oplossing die de oogst kan maximaliseren terwijl de economische en ecologische kosten van voedselproductie beperkt worden. Om dit te kunnen doen is gedetailleerde informatie over de staat van het gewas nodig. Remote sensing is een manier om biofysische informatie, waaronder stikstof gehaltes en biomassa, te verkrijgen. De informatie van een individuele sensor is echter vaak niet genoeg om aan de hoge eisen betreft ruimtelijke en temporele resolutie te voldoen. Deze studie combineert daarom de informatie afkomstig van verschillende sensoren, namelijk multispectrale satelliet beelden (Formosat-2) en hyperspectral Unmanned Aerial Vehicle (UAV) beelden van een aardappel veld, in een poging om aan de hoge informatie eisen van precisielandbouw te voldoen. Ten eerste werd gebruik gemaakt van datafusie om de acht Formosat-2 beelden met een resolutie van 8 m te combineren met de vier UAV beelden met een resolutie van 1 m. De resulterende dataset bestaat uit acht beelden met een resolutie van 1 m. Twee methodes werden toegepast, de zogenaamde STARFM methode en een unmixing-based methode. De unmixing-based methode produceerde beelden met een hoge correlatie op de Leaf Area Index (LAI) (rs= 0.866) en chlorofyl gehalte (rs=0.884) gemeten op veldnieveau. De STARFM methode presteerde slechter, met correlaties van respectievelijk rs=0.477 en rs=0.431. Ten tweede werden Spectral-Temporal Reflectance Surfaces (STRSs) ontwikkeld die een dagelijks spectrum weergeven met 101 spectrale banden. Om dit te doen is een nieuwe STRS methode gebaseerd op de Bayesiaanse theorie ontwikkeld. Deze produceert realistische spectra met een overeenkomstige onzekerheid. Deze STRSs vertoonden hoge correlaties met de LAI (rs=0.858) en het chlorofyl gehalte (rs=0.788) gemeten op veldnieveau. De bruikbaarheid van deze twee soorten datasets werd geanalyseerd door middel van de berekening van een aantal vegetatie-indexen. De resultaten tonen dat de multi-sensor datasets capabel zijn om significante verschillen in de groei van gewassen vast te stellen tijdens het groeiseizoen zelf. Bovendien werden regressiemodellen toegepast om de bruikbaarheid van de datasets voor oogst voorspellingen. De unmixing-based datafusie verklaarde 52.7% van de variatie in oogst, terwijl de STRS 72.9% van de variabiliteit verklaarden. De resultaten van het huidige onderzoek tonen aan dat de beperkingen van een individuele sensor grotendeels overtroffen kunnen worden door het gebruik van meerdere sensoren. Het combineren van verschillende sensoren, of het nu Formosat-2 en UAV beelden zijn of andere ruimtelijke informatiebronnen, kan de hoge informatie eisen van de precisielandbouw tegemoet komen.In the context of threatened global food security, precision agriculture is one strategy to maximize yield to meet the increased demands of food, while minimizing both economic and environmental costs of food production. This is done by applying variable management strategies, which means the fertilizer or irrigation rates within a field are adjusted according to the crop needs in that specific part of the field. This implies that accurate crop status information must be available regularly for many different points in the field. Remote sensing can provide this information, but it is difficult to meet the information requirements when using only one sensor. For example, satellites collect imagery regularly and over large areas, but may be blocked by clouds. Unmanned Aerial Vehicles (UAVs), commonly known as drones, are more flexible but have higher operational costs. The purpose of this study was to use fusion methods to combine satellite (Formosat-2) with UAV imagery of a potato field in the Netherlands. Firstly, data fusion was applied. The eight Formosat-2 images with 8 m x 8 m pixels were combined with four UAV images with 1 m x 1 m pixels to obtain a new dataset of eight images with 1 m x 1 m pixels. Unmixing-based data fusion produced images which had a high correlation to field measurements obtained from the potato field during the growing season. The results of a second data fusion method, STARFM, were less reliable in this study. The UAV images were hyperspectral, meaning they contained very detailed information spanning a large part of the electromagnetic spectrum. Much of this information was lost in the data fusion methods because the Formosat-2 images were multispectral, representing a more limited portion of the spectrum. Therefore, a second analysis investigated the use of Spectral-Temporal Reflectance Surfaces (STRS), which allow information from different portions of the electromagnetic spectrum to be combined. These STRS provided daily hyperspectral observations, which were also verified as accurate by comparing them to reference data. Finally, this study demonstrated the ability of both data fusion and STRS to identify which parts of the potato field had lower photosynthetic production during the growing season. Data fusion was capable of explaining 52.7% of the yield variation through regression models, whereas the STRS explained 72.9%. To conclude, this study indicates how to combine crop status information from different sensors to support precision agriculture management decisions

Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape

The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V