Linking in situ LAI and Fine Resolution Remote Sensing Data to Map Reference LAI over Cropland and Grassland Using Geostatistical Regression Method

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bia

MODIS land cover and LAI Collection 4 product quality across nine sites in the western hemisphere

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addresse

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI \u3c 2m2/m2, AGB \u3c 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI \u3e 2m2/m2, AGB \u3e 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management

Balanço de energia com base no modelo S-SEBI sobre gramíneas em Barrax, Espanha e no bioma Pampa do sul do Brasil

No Brasil, existem seis biomas, sendo eles Amazônia, Mata Atlântica, Cerrado, Caatinga, Pantanal e Pampa. Cada bioma possui características únicas e importantes para a manutenção dos seus processos ecossistêmicos. Neste sentido, no bioma Pampa há uma dinâmica socioambiental que influencia a vegetação, o manejo agrícola e o modo de vida da população local. Este bioma é único no mundo porque traz na vegetação rasteira sua fonte de biomassa e energia como em nenhum outro ecossistema, seus campos nativos são os responsáveis pela conservação e preservação dos recursos hídricos, da fauna silvestre e da biodiversidade. A supressão da vegetação nativa deste bioma para a monocultura de grãos compromete a manutenção da biodiversidade e gera impactos nos recursos naturais, alterando as suas condições ambientais, a disponibilidade de água e a temperatura de superfície. Além disso, as mudanças climáticas têm modificado os componentes do Balanço de Energia (BE). Em relação ao balanço energético este bioma tem, no estado do Rio Grande do Sul, a mesma importância climática que as florestas em regiões tropicais, já que cobre 63% do Estado e possui influência nas dinâmicas atmosféricas. Sendo assim, o objetivo deste trabalho é avaliar as particularidades ambientais do BE e do cálculo de evapotranspiração (ET) no bioma Pampa. A ET é a responsável pelas interações da biosfera- atmosfera-hidrosfera. Estas interações se dão por utilizar energia eletromagnética para a formação de vapor d’água a partir da transpiração vegetal e da evaporação da água. O uso do Sensoriamento Remoto tem sido eficaz nas estimativas de fluxo de calor sensível e fluxo de calor latente por diferentes métodos, porém a aplicação de forma operacional, a heterogeneidade da superfície e a influência da temperatura de superfície (Ts) são desafios deste trabalho. O modelo S-SEBI para recuperação de dados de ET foi avaliado no bioma Pampa e em Barrax, um sítio de validação localizado no mediterrâneo espanhol. O modelo demonstrou ser eficaz em vegetação campestre, além de ser menos dependente da Ts em relação a outros modelos reportados na literatura. Os resultados deste trabalho visam contribuir para a geração de melhor qualidade de dados de ET em futuras análises de mudanças de uso do solo, mudanças climáticas e gestão dos recursos hídricos para todo o bioma Pampa.In Brazil, there are six biomes, namely the Amazon, Atlantic Forest, Cerrado, Caatinga, Pantanal, and Pampa. Each biome has unique and important characteristics for the maintenance of the ecosystemic processes of each environment. In this sense, in the Pampa biome there is a socio-environmental dynamic that influences the vegetation, agricultural management, and the way of life of the local population. This biome is unique in the world because it brings in its undergrowth vegetation its source of biomass and energy like no other ecosystem; its native grasslands are responsible for the conservation and preservation of water resources, wildlife, and biodiversity. The suppression of the native vegetation of this biome for the monoculture of grains compromises the maintenance of biodiversity and generates impacts on natural resources, altering the environmental conditions of the ecosystem, water availability, and surface temperature. In addition, climate change has modified the components of the Energy Balance (EB). In relation to the energy balance, in the state of Rio Grande do Sul, this biome has the same climatic importance as the forests in tropical regions, since it covers 63% of the state and influences the atmospheric dynamics. Therefore, the objective of this work is to evaluate the environmental particularities of BE and the calculation of evapotranspiration (ET) in the Pampa biome. ET is responsible for biosphere-atmosphere-hydrosphere interactions. These interactions occur by using electromagnetic energy for the formation of water vapor from plant transpiration and water evaporation. The use of Remote Sensing has been effective in estimating sensible heat flux and latent heat flux by different methods, but the application in an operational way, the heterogeneity of the surface and the influence of the surface temperature (Ts) are challenges of this work. The S-SEBI model for ET data retrieval was evaluated in the Pampa biome and in Barrax, a validation site located in the Spanish Mediterranean. The model proved to be effective in grassland vegetation, and is less dependent on Ts compared to other models reported in the literature. The results of this work aim to contribute to the generation of better quality ET data in future analyses of land use change, climate change, and water resource management for the entire Pampa biome

People, Institutions, and Pixels: Linking Remote Sensing and Social Science to Understand Social Adaptation to Environmental Change.

This research presents an interdisciplinary approach, which links theories from grassland ecology and institutional economics and methods from remote sensing, field ecological measurements, household survey, statistical modeling, and agent-based computational modeling, to study the dynamics of grassland social-ecological systems on the Mongolian plateau, including Mongolia and Inner Mongolia Autonomous Region, China, and social adaptation to climate change and ecosystem degradation. A range of research questions in the fields of remote sensing of vegetation, drivers and mechanisms of resource dynamics, and societal adaptation to environmental change were addressed at regional and local scales. Using a remote sensing based light-use efficiency model, I estimated annual grassland net primary productivity on the Mongolian plateau over the past three decades and analyzed the spatial-temporal dynamics of annual grassland net primary productivity in response to climate variability and change. In order to account for the insufficiency of using multispectral images to map grassland communities and monitor grassland dynamics, especially grassland degradation, I analyzed the potential for using hyperspectral remote sensing to detect the quantity and quality of dominant grassland communities across ecological gradients of the Inner Mongolian grasslands, based on field data collected across a large geographic area. The dynamics of grassland productivity on the Mongolian plateau over the past decades was interpreted both qualitatively and quantitatively. I used spatial panel data models to identify the biophysical and socioeconomic factors driving the interannual dynamics of grassland net primary productivity across agro-ecological zones on the Mongolian plateau over the past three decades. Social adaptations to climate change and grassland degradation on the Mongolian plateau was studied at both household and community levels. A household survey was designed and implemented across ecological gradients of Mongolia (210 households) and Inner Mongolia, China (540 households), to study livelihood adaptation practices of herders to environmental change. Informed by the empirical studies, I built an agent-based computational model to explore social-ecological outcomes of pasture use under alternative institutional (i.e., grazing sedentarization, pasture rental markets, and reciprocal use of pastures) and climatic (i.e., frequencies of climate hazards) scenarios.PHDNatural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97961/1/junw_1.pd

Spatial Estimations of Soil Properties for Physically-based Soil Erosion Modelling in the Three Gorges Reservoir Area, Central China

Soils present a central medium for processes between the environmental spheres, and therefore play a key role in the functioning of terrestrial ecosystems. However, soil erosion as a natural force of landscape evolution adversely affects the capacity of soils to support ecosystem services. Moreover, inadequate agricultural practices, deforestation, and construction activities amplify natural soil loss rates and transform soil erosion to a major threat for managed ecosystems worldwide. Particularly, the Three Gorges Reservoir Area in China is highly susceptible to soil erosion by water. This is attributable to unfavorable environmental conditions, such as rainfall events of high intensity and steep slope inclinations in areas of extensive, but small-scale crop cultivation. Moreover, in the course of the impoundment of the Yangtze River in the area of the Three Gorges, resettlements and accompanied deforestation reinforced the risk of hazardous soil erosion, which attenuates soil productivity and threatens the functioning of the reservoir. Therefore, conservation measures to stabilize steep sloping surfaces have been implemented to mitigate the hazardous effects of soil erosion. However, to assess the conservation measures an efficient tool is required to identify spatial soil erosion patterns in small, mountainous, and data scarce catchments within the Three Gorges Reservoir Area. The present thesis aims to provide an efficient modelling framework that facilitates a detailed quantification of sediment reallocations due to erosive rainfall-runoff events. Therefore, Digital Soil Mapping techniques based on Latin Hypercube Sampling and Random Forest regression were applied to derive spatially distributed data on soil properties and to furnish a physically- and event-based soil erosion model. The soil sampling design was optimized to address the difficult terrain, an integrative use of legacy soil samples, and a reduced sample set size. Furthermore, the present thesis introduces a spatial uncertainty measure, which was used to identify areas for additional sampling to further refine initially processed soil property maps. In addition, continuous data on rainfall, runoff, and sediment yields were obtained to identify erosive rainfall-runoff events and to calibrate the physically-based soil erosion model EROSION 3D. Evaluation of the hypercube sampling design was conducted by comparing it to a simulated Latin Hypercube design without constraints in terms of operability and efficiency adjustments. Using the optimized sample set size of n = 30, the proposed sample design adequately reproduced the variation of terrain parameters, which served as proxies on the target soil properties of coarse, medium, and fine topsoil sand contents. Furthermore, the validity of the approach was assessed by estimating the spatial distribution of the target soil properties and validating the results independently. The results show convincing accuracies with R²-values between 0.59 and 0.71. The adequacy of the uncertainty-guided sampling for refining initial mapping approaches was evaluated by comparing the refined maps of topsoil silt and clay contents to the initial and further mapping approaches that exclusively used random samples from the entire study area. For the comparative analysis, the quality of the approaches was assessed by independent, bootstrap-, and cross-validation. The refined mapping approach performs best, showing a reduced spatial uncertainty of 31% for topsoil silt and 27% for topsoil clay compared to the initial approaches. Using independent validation, the accuracy increases by similar proportions, showing an accuracy of R² = 0.59 for silt and R² = 0.56 for clay. The EROSION 3D model runs were evaluated using the measured sediment yields. The model performs well for large events (sediment yield > 1 Mg) with an average individual model error of 7.5%, while small events show an average error of 36.2%. The focus of analysis was led on the large events to evaluate reallocation patterns. Soil losses occur on approximately 11.1% of the study area with an average soil loss rate of 49.9 Mg ha-1. Soil loss mainly occurs on crop rotation areas with a spatial proportion of 69.2% for ‘corn-rapeseed’ and 69.1% for ‘potato-cabbage’. Deposition occurs on 11% of the study area. Forested areas (9.7%), infrastructure (41%), cropland (corn-rapeseed: 13.6%, potato-cabbage: 11.3%), and grassland (18.4%) are affected by deposition. Since the vast majority of annual sediment yields (80.3%) were associated to a few large erosive events, the modelling framework can be recommended to identify sediment reallocations and to assess conservation measures in small catchments in the Three Gorges Reservoir Area

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R2=0.86, RMSE=0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates were aggregated to the resolution of the 1-km MODIS LAI product and compared to temporally-coincident MODIS retrievals. Differences in the MODIS and lidar-derived values of LAI were grouped and analyzed by several different factors, including MODIS retrieval algorithm, sun/sensor geometry, and sub-pixel heterogeneity in both vegetation and terrain characteristics. Of particular interest is the disparity in the results when MODIS LAI was analyzed according to algorithm retrieval class. We observed relatively good agreement between lidar-derived and MODIS LAI values for pixels retrieved with the main RT algorithm without saturation for LAI LAI≤4. Moreover, for the entire range of LAI values, considerable overestimation of LAI (relative to lidar-derived LAI) occurred when either the main RT with saturation or back-up algorithm retrievals were used to populate the composite product regardless of sub-pixel vegetation structural complexity or sun/sensor geometry. These results are significant because algorithm retrievals based on the main radiative transfer algorithm with or without saturation are characterized as suitable for validation and subsequent ecosystem modeling, yet the magnitude of difference appears to be specific to retrieval quality class and vegetation structural characteristics

Appraisal of Water Quality Measurements for Canal and Tube Well Water Systems for Agriculture Irrigation in Rechna Doab, Pakistan

The present study was an attempt to assess the surface and ground water quality for irrigation suitability in Rechna Doab. Irrigation water quality at canals and tube well water were analyzed by physicochemical parameters including pH, Electric Conductivity (EC), important cations such as Calcium (Ca2+) Magnesium (Mg2+), Potassium (K+), Sodium (Na+), important anions such as Chloride (Cl-), Bicarbonate (HCO3-), Sulphate (SO42-), three heavy metals including Zinc (Zn), Nickel (Ni) and Copper (Cu). Twelve water samples were collected from the main canals (Lower Gogera canal, Jhang branch canal and Rakh branch canal) while fifty water samples were collected from the tube wells. Statistically, data were analyzed by generating correlation coefficients. Canal water quality parameters i.e. Sodium Adsorption Ratio (SAR), Magnesium Adsorption Ratio (MAR), Sodium Percentage (Na %), Kelly Ratio (KR), Soluble Sodium Percentage (SSP), Residual Sodium Bicarbonate (RSBC), Permeability Index (PI) and Potential Salinity (PS) with their mean values 0.16,38.18, 8.03, 0.08, 10.17, 0.08, 28.34 and 0.024 respectively were calculated. Piper and Durov diagrammatic representations provided the suitability of the canal water regarding ionic composition. Results revealed that the status of the canal water was fit for agriculture. On the contrary, the data about Electric Conductivity (EC), Sodium Adsorption Ratio (SAR) and Residual Sodium Carbonate (RSC) of tube well water (with their maximum values 4.80, 29.65 and 13.60, respectively) was exceeding the FAO limits owing of sodium hazards. Thus, the scenario of groundwater is alarming due to unfit status of tube well water regarding irrigation purposes. Out of total 50 water samples of tube wells, 11 samples were found to be fit. While 39 samples were unfit for crop irrigation. Geo-statistical analysis was performed by using Inverse Distance Weighted (IDW) technique created in Arc map

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