시공간 해상도 향상을 통한 식생 변화 모니터링

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2023. 2. 류영렬.육상 생태계에서 대기권과 생물권의 상호 작용을 이해하기 위해서는 식생 변화의 모니터링이 필요하다. 이 때, 위성영상은 지표면을 관측하여 식생지도를 제공할 수 있지만, 지표변화의 상세한 정보는 구름이나 위성 이미지의 공간 해상도에 의해 제한되었다. 또한 위성영상의 시공간 해상도가 식생지도를 통한 광합성 모니터링에 미치는 영향은 완전히 밝혀지지 않았다. 본 논문에서는 고해상도 식생 지도를 일단위로 생성하기 위성 영상의 시공간 해상도를 향상시키는 것을 목표로 하였다. 고해상도 위성영상을 활용한 식생 변화 모니터링을 시공간적으로 확장하기 위해 1) 정지궤도 위성을 활용한 영상융합을 통해 시간해상도 향상, 2) 적대적생성네트워크를 활용한 공간해상도 향상, 3) 시공간해상도가 높은 위성영상을 토지피복이 균질하지 않은 공간에서 식물 광합성 모니터링을 수행하였다. 이처럼, 위성기반 원격탐지에서 새로운 기술이 등장함에 따라 현재 및 과거의 위성영상은 시공간 해상도 측면에서 향상되어 식생 변화의 모니터링 할 수 있다. 제2장에서는 정지궤도위성영상을 활용하는 시공간 영상융합으로 식물의 광합성을 모니터링 했을 때, 시간해상도가 향상됨을 보였다. 시공간 영상융합 시, 구름탐지, 양방향 반사 함수 조정, 공간 등록, 시공간 융합, 시공간 결측치 보완 등의 과정을 거친다. 이 영상융합 산출물은 경작관리 등으로 식생 지수의 연간 변동이 큰 두 장소(농경지와 낙엽수림)에서 평가하였다. 그 결과, 시공간 영상융합 산출물은 결측치 없이 현장관측을 예측하였다 (R2 = 0.71, 상대 편향 = 5.64% 농경지; R2 = 0.79, 상대 편향 = -13.8%, 활엽수림). 시공간 영상융합은 식생 지도의 시공간 해상도를 점진적으로 개선하여, 식물 생장기동안 위성영상이 현장 관측을 과소 평가를 줄였다. 영상융합은 높은 시공간 해상도로 광합성 지도를 일간격으로 생성하기에 이를 활용하여 위성 영상의 제한된 시공간 해상도로 밝혀지지 않은 식물변화의 과정을 발견하길 기대한다. 식생의 공간분포은 정밀농업과 토지 피복 변화 모니터링을 위해 필수적이다. 고해상도 위성영상으로 지구 표면을 관측하는 것을 용이하게 해졌다. 특히 Planet Fusion은 초소형위성군 데이터를 최대한 활용해 데이터 결측이 없는 3m 공간 해상도의 지표 표면 반사도이다. 그러나 과거 위성 센서(Landsat의 경우 30~60m)의 공간 해상도는 식생의 공간적 변화를 상세 분석하는 것을 제한했다. 제3장에서는 Landsat 데이터의 공간 해상도를 향상하기 위해 Planet Fusion 및 Landsat 8 데이터를 사용하여 이중 적대적 생성 네트워크(the dual RSS-GAN)를 학습시켜, 고해상도 정규화 식생 지수(NDVI)와 식물 근적외선 반사(NIRv)도를 생성하는 한다. 타워기반 현장 식생지수(최대 8년)와 드론기반 초분광지도로 the dual RSS-GAN의 성능을 대한민국 내 두 대상지(농경지와 활엽수림)에서 평가했다. The dual RSS-GAN은 Landsat 8 영상의 공간해상도를 향상시켜 공간 표현을 보완하고 식생 지수의 계절적 변화를 포착했다(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 지수가 현장에 비해 과소 평가되는 것을 완화했다. 현장 관측에 비해 이중 RSS-GAN과 Landsat 8의 상대 편향 값 각각 -0.8% 에서 -1.5%, -10.3% 에서 -4.6% 였다. 이러한 개선은 Planet Fusion의 공간정보를 이중 RSS-GAN로 학습하였기에 가능했다. 헤당 연구 결과는 Landsat 영상의 공간 해상도를 향상시켜 숨겨진 공간 정보를 제공하는 새로운 접근 방식이다. 고해상도에서 식물 광합성 지도는 토지피복이 복잡한 공간에서 탄소 순환 모니터링시 필수적이다. 그러나 Sentinel-2, Landsat 및 MODIS와 같이 태양 동조 궤도에 있는 위성은 공간 해상도가 높거나 시간 해상도 높은 위성영상만 제공할 수 있다. 최근 발사된 초소형위성군은 이러한 해상도 한계을 극복할 수 있다. 특히 Planet Fusion은 초소형위성 자료의 시공간 해상도로 지표면을 관측할 수 있다. 4장에서, Planet Fusion 지표반사도를 이용하여 식생에서 반사된 근적외선 복사(NIRvP)를 3m 해상도 지도를 일간격으로 생성했다. 그런 다음 미국 캘리포니아주 새크라멘토-샌 호아킨 델타의 플럭스 타워 네트워크 데이터와 비교하여 식물 광합성을 추정하기 위한 NIRvP 지도의 성능을 평가하였다. 전체적으로 NIRvP 지도는 습지의 잦은 수위 변화에도 불구하고 개별 대상지의 식물 광합성의 시간적 변화를 포착하였다. 그러나 대상지 전체에 대한 NIRvP 지도와 식물 광합성 사이의 관계는 NIRvP 지도를 플럭스 타워 관측범위와 일치시킬 때만 높은 상관관계를 보였다. 관측범위를 일치시킬 경우, NIRvP 지도는 식물 광합성을 추정하는 데 있어 현장 NIRvP보다 우수한 성능을 보였다. 이러한 성능 차이는 플럭스 타워 관측범위를 일치시킬 때, 연구 대상지 간의 NIRvP-식물 광합성 관계의 기울기가 일관성을 보였기 때문이다. 본 연구 결과는 위성 관측을 플럭스 타워 관측범위와 일치시키는 것의 중요성을 보여주고 높은 시공간 해상도로 식물 광합성을 원격으로 모니터링하는 초소형위성군 자료의 잠재력을 보여준다.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed. In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution. In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery. Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from −3.2% to 1.2% and −12.4% to −3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2 1. Background 2 1.1 Daily gap-free surface reflectance using geostationary satellite products 2 1.2 Monitoring past vegetation changes with high-spatial-resolution 3 1.3 High spatiotemporal resolution vegetation photosynthesis maps 4 2. Purpose of Research 4 Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6 1. Introduction 6 2. Methods 11 2.1 Study sites 11 2.2 In situ measurements 13 2.3 Satellite products 14 2.4 Integrated system 17 2.5 Canopy photosynthesis 21 2.6 Evaluation 23 3. Results and discussion 24 3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24 3.2 Comparison of STIF NIRvP with in situ NIRvP 28 4. Conclusion 31 Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32 1. Introduction 32 2. Methods 38 2.1 Real-ESRGAN model 38 2.2 Study sites 40 2.3 In situ measurements 42 2.4 Vegetation index 44 2.5 Satellite data 45 2.6 Planet Fusion 48 2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49 2.8 Evaluation 54 3. Results 57 3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57 3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60 3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63 3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66 4. Discussion 70 4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70 4.2 CubeSat data in the dual RSS-GAN model 72 4.3 Perspectives and limitations 73 5. Conclusion 78 Appendices 79 Supplementary material 82 Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85 1. Introduction 85 2. Methods 89 2.1 Study sites 89 2.2 In situ measurements 92 2.3 Planet Fusion NIRvP 94 2.4 Flux footprint model 98 2.5 Evaluation 98 3. Results 105 3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105 3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108 3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114 4. Discussion 118 4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118 4.2 Roles of radiation component in GPP mapping 123 4.3 Limitations and perspectives 126 5. Conclusion 133 Appendix 135 Supplementary Materials 144 Chapter 5. Conclusion 153 Bibliography 155 Abstract in Korea 199 Acknowledgements 202박

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

An evaluation of Multiple Endmember Spectral Mixture Analysis applied to Landsat 8 OLI images for mapping land cover in southern Africa\u27s Savanna.

Mapping land cover in southern Africa’s savannas using traditional pixel based remote sensing techniques can be very challenging due to the heterogeneity of its vegetation structure and the spectral difficulty in separating similar land covers across various land uses. In order to overcome these complications, a Multiple Endmember Spectral Mixture Analysis (MESMA) provides a potential remote sensing approach to quantify spectral variation in the physical environment at a subpixel level. The MESMA approach was applied in the study area of the Mayuni Conservancy, in Namibia. Results show that 32.3% of the study area is covered by photosynthetic vegetation (PV), 32.0% by non-photosynthetic vegetation (NPV), 25.2% by bare soil (B) and 10.6% by shade. Post-classification validation shows that MESMA presented a moderate performance in estimating the proportions of land cover types in the study area. However the validation process is limited to the available resources and carries great subjectivity. It is concluded that future research on the matter should include a more consistent investigation on the endmember selection methodology and expand the study area inside of the same ecosystem

Spatiotemporally representative and cost-efficient sampling design for validation activities in wanglang experimental site

Altres ajuts: EC Copernicus Global Land Service (CGLOPS-1, 199494-JRC.Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI) imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA) between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study inWanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7%) compared to the single-temporally constrained (OA = 68.7%) and the random sampling (OA = 63.1%) strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet) which need a spatially distributed and temporally fixed sampling design

Landsat-based operational wheat area estimation model for Punjab, Pakistan

Wheat in Punjab province of Pakistan is grown during the Rabi (winter) season within a heterogeneous smallholder agricultural system subject to a range of pressures including water scarcity, climate change and variability, and management practices. Punjab is the breadbasket of Pakistan, representing over 70% of national wheat production. Timely estimation of cultivated wheat area can serve to inform decision-making in managing harvests with regard to markets and food security. The current wheat area and yield reporting system, operated by the Punjab Crop Reporting Service (CRS) delivers crop forecasts several months after harvest. The delayed production data cannot contribute to in-season decision support systems. There is a need for an alternative cost-effective, efficient and timely approach on producing wheat area estimates, in ensuring food security for the millions of people in Pakistan. Landsat data, medium spatial and temporal resolutions, offer a data source for characterizing wheat in smallholder agriculture landscapes. This dissertation presents methods for operational mapping of wheat cultivate area using within growing season Landsat time-series data. In addition to maps of wheat cover in Punjab, probability-based samples of in-situ reference data were allocated using the map as a stratifier. A two-stage probability based cluster field sample was used to estimate area and assess map accuracies. The before-harvest wheat area estimates from field-based sampling and Landsat map were found to be comparable to official post-harvest data produced by the CRS Punjab. This research concluded that Landsat medium resolution data has sufficient spatial and temporal coverage for successful wall-to-wall mapping of wheat in Punjab’s smallholder agricultural system. Freely available coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems; commercial high spatial resolution satellite data are often advocated as an alternative for characterizing fine-scale land tenure agricultural systems such as that found in Punjab. Commercial 5 m spatial resolution RapidEye data from the peak of the winter wheat growing season were used as sub-pixel training data in mapping wheat with the growing season free 30 m Landsat time series data from the 2014-15 growing season. The use of RapidEye to calibrate mapping algorithms did not produce significantly higher overall accuracies ( ± standard error) compared to traditional whole pixel training of Landsat-based 30 m data. Continuous wheat mapping yielded an overall accuracy of 88% (SE = ±4%) in comparison to 87% (SE = ±4%) for categorical wheat mapping, leading to the finding that sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other similar landscapes, training data for supervised classification may be collected directly from Landsat images with probability based stratified random sampling as reference data without the need for high-resolution reference imagery. The research concluded by exploring the use of automated models in wheat area mapping and area estimation using growing season Landsat time-series data. The automated classification tree model resulted in wheat / not wheat maps with comparable accuracies compared to results achieved with traditional manual training. In estimating area, automated wheat maps from previous growing seasons can serve as a stratifier in the allocation of current season in-situ reference data, and current growing season maps can serve as an auxiliary variable in model-assisted area estimation procedures. The research demonstrated operational implementation of robust automated mapping in generating timely, accurate, and precise wheat area estimates. Such information is a critical input to policy decisions, and can help to ensure appropriate post-harvest grain management to address situations arising from surpluses or shortages in crop production

ENHANCING CONSERVATION WITH HIGH RESOLUTION PRODUCTIVITY DATASETS FOR THE CONTERMINOUS UNITED STATES

Human driven alteration of the earth’s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth’s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain. The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Land-use and land-cover dynamics monitored by NDVI multitemporal analysis in a selected southern amazonian area (Brazil) for the last three decades

This study aims to analyse the dynamics of land-use and land-cover (LULC) in a selected southern Amazonian area (Brazil),monitoring and distinguishing trajectories in NDVI (Normalized Difference Vegetation Index) variations for the last three decades. The area, with a total of 17336 km², has been subject to significant LULC changes associated with deforestation progress and use of fire. Considering available Landsat time series, it was selected an image per year from 1984 to 2013 (path/row -231/66), at a particular period of year, atmospherically corrected using LEDAPS tools. NDVIs values were generated for each selected image. Furthermore, the images of 1984 and 2010 still underwent a classification of LULC differentiate five categories: water, forest, secondary/degraded forest, savannah/pasture and crop/bare soil. The trajectories in NDVI variation values were analysed by R software, considering intersections of classified categories. The pixels identified as forests on the images of 1984 and 2010 displayed stable trajectories of NDVI values, with average value 0.824 and coefficient of variation 3.9%. While the pixels of savannah/pasture, which was periodically affected by fire, had an average NDVI value 0.585 and coefficient of variation 15,1%. The main regressive trajectory was the transition “forest to crop/bare soil", identifying 1999 as the starting point in the drop in NDVI values, associated with an increase of the deforested areas. Therefore, the results show distinct trajectories associated with NDVIs and LULC changes that assist in better understanding the dynamics of ecological processes and the human impacts operating in the area

Characterizing Spatiotemporal Patterns of White Mold in Soybean across South Dakota Using Remote Sensing

Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to previous reports, environmental conditions corresponding to high yield potential are most conducive for white mold development. These conditions include cool temperature (12-24 °C), continued wet and moist conditions (70-120 h) generally resulting from rain, but the disease development requires the presence of a susceptible soybean variety. To better understand white mold development in the field, there is a need to investigate its spatiotemoral characteristics and provide accurate estimates of the damages that white mold may cause. Current and accurate data about white mold are scarce, especially at county or larger scale. Studies that explored the characteristics of white mold were generally field oriented and local in scale, and when the spectral characteristics were investigated, the authors used spectroradiometers that are not accessible to farmers and to the general public and are mostly used for experimental modeling. This study employed free remote sensing Landsat 8 images to quantify white mold in South Dakota. Images acquired in May and July were used to map the land cover and extract the soybean mask, while an image acquired in August was used to map and quantify white mold using the random forest algorithm. The land cover map was produced with an overall accuracy of 95% while white mold was mapped with an overall accuracy of 99%. White mold area estimates were respectively 132 km2, 88 km2, and 190 km2, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties. This study also explored the spatial characteristics of white mold in soybean fields and its impact on yield. The yield distribution exhibited a significant positive spatial autocorrelation (Moran’s I = 0.38, p-value \u3c 0.001 for Moody field, Moran’s I = 0.45, p-value \u3c 0.001, for Marshall field) as an evidence of clustering. Significant clusters could be observed in white mold areas (low-low clusters) or in healthy soybeans (high-high clusters). The yield loss caused by the worst white mold was estimated at 36% and 56% respectively for the Moody and the Marshall fields, with the most accurate loss estimation occurring between late August and early September. Finally, this study modeled the temporal evolution of white mold using a logistic regression analysis in which the white mold was modeled as a function of the NDVI. The model was successful, but further improved by the inclusion of the Day of the Year (DOY). The respective areas under the curves (AUC) were 0.95 for NDVI and 0.99 for NDVI+DOY models. A comparison of the NDVI temporal change between different sites showed that white mold temporal development was affected by the site location, which could be influenced by many local parameters such as the soil properties, the local elevation, management practices, or weather parameters. This study showed the importance of freely available remotely sensed satellite images in the estimation of crop disease areas and in the characterization of the spatial and temporal patterns of crop disease; this could help in timely disease damage assessment