Retrieval of Salt Marsh Above-ground Biomass From High-spatial Resolution Hyperspectral Imagery Using PROSAIL

Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( R2=0.73 ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Quantitative Mapping of Soil Property Based on Laboratory and Airborne Hyperspectral Data Using Machine Learning

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 11

Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas

Fire risk assessment is one of the most important components in the management of fire that offers the framework for monitoring fire risk conditions. Whilst monitoring fire risk conditions commonly revolved around field data, Remote Sensing (RS) plays key role in quantifying and monitoring fire risk indicators. This study presents a review of remote sensing data and techniques for fire risk monitoring and assessment with a particular emphasis on its implications for wildfire risk mapping in protected areas. Firstly, we concentrate on RS derived variables employed to monitor fire risk conditions for fire risk assessment. Thereafter, an evaluation of the prominent RS platforms such as Broadband, Hyperspectral and Active sensors that have been utilized for wildfire risk assessment. Furthermore, we demonstrate the effectiveness in obtaining information that has operational use or immediate potentials for operational application in protected areas (PAs). RS techniques that involve extraction of landscape information from imagery were summarised. The review concludes that in practice, fire risk assessment that consider all variables/indicators that influence fire risk is impossible to establish, however it is imperative to incorporate indicators or variables of very high heterogeneous and “multi-sensoral or multivariate fire risk index approach for fire risk assessment in PA.Keywords: Protected Areas, Fire Risk conditions; Remote Sensing, Wildfire risk assessmen