Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion

Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and RapidEye 5 m resolution images were fused to produce synthetic RapidEye images, from June 2011 to July 2012. We evaluated the performance of the algorithm by comparing predicted surface reflectance values to real RapidEye images. Our results show that ESTARFM predictions are accurate, with a coefficient of determination for the red band 0.80 < R2 < 0.92, and for the near-infrared band 0.83 < R2 < 0.93, a mean relative bias between 6% and 12% for the red band and 4% to 9% in the near-infrared band. Heterogeneous vegetation at sub-MODIS resolution is captured adequately: A comparison of NDVI time series derived from RapidEye and ESTARFM data shows that the characteristic phenological dynamics of different vegetation types are reproduced well. We conclude that the ESTARFM algorithm allows us to produce synthetic remote sensing images at high spatial combined with high temporal resolution and so provides valuable information on vegetation dynamics in semi-arid, heterogeneous rangeland landscapes

Estimativa da degradação de pastagens cultivadas do Cerrado mineiro com base na técnica de Eficiência no Uso da Água (WUE - Water Use Efficiency)

Dissertação (mestrado)—Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação em Geociências Aplicadas, 2017.Estudos espaço-temporais das condições agronômicas de pastagens cultivadas são essenciais para elaboração de diretrizes e ações de políticas público-privadas para aumentar a produtividade da pecuária. Neste contexto, este trabalho tem por finalidade avaliar as condições de degradação das pastagens cultivadas no Cerrado mineiro, por meio de estimativas de Eficiência no Uso da Água (WUE – water use eficiency) no período de 2003 a 2014. WUE corresponde a uma relação entre produtividade primária líquida (NPP) e evapotranspiração real (ETR). O mapa de pastagens cultivadas do Cerrado mineiro utilizado nesse estudo foi obtido pelo projeto TerraClass Cerrado de 2013, coordenado pelo Ministério do Meio Ambiente (MMA). As séries temporais do índice de vegetação realçado (EVI) das plataformas Terra e Aqua do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) (produtos MOD/MYD13Q1) foram utilizadas para estimar e descrever a variabilidade espaço-temporal da NPP, por meio do aplicativo Timesat. As imagens mensais de ETR foram obtidas do produto MOD16A2 e comparadas com dados de ETR oriundas de 20 estações meteorológicas do Instituto Nacional de Meteorologia (INMET). A NPP média das pastagens cultivadas no Cerrado mineiro no período de 12 anos foi de 5,98. As maiores taxas de ETR estiveram associadas aos valores mais elevados da NPP. Em geral, áreas de pastagens cultivadas apresentam ETR média anual de 690 mm, com 67% da ETR ocorrendo durante a estação chuvosa (outubro a abril). Tendências positivas da ETR foram encontradas em mais de 80% das áreas de pastagens cultivadas do Cerrado mineiro. Valor médio de WUE no período considerado foi de 0,08. Ainda de acordo com esses dados de WUE, 46% (5,48 milhões de hectares) do total de áreas de pastagens cultivadas no Cerrado mineiro apresentaram processo de degradação biológica. As mesorregiões Triângulo Mineiro/Alto Parnaíba e Norte de Minas apresentaram as maiores produtividades e as menores perdas de água para a atmosfera, portanto, as pastagens mais produtivas. A técnica de WUE permitiu avaliar as condições de degradação das pastagens cultivadas do Cerrado mineiro. A restauração dessas pastagens degradadas pode auxiliar no aumento da produção da carne bovina dessa região, auxiliando na redução da taxa de conversão da vegetação natural para novas áreas de pastagens.Spatio-temporal studies of the agronomic conditions of cultivated pastures are essential for the elaboration of guidelines and actions of public-private policies to increase the productivity of livestock. In this context, this study aims to evaluate the degradation conditions of the cultivated pastures found in the Minas Gerais State covered by the Cerrado biome, using water use efficiency (WUE) estimates from 2003 to 2014. WUE corresponds to a relation between net primary productivity (NPP) and real evapotranspiration (ETR). The cultivated pasture map used in this study was obtained by the TerraClass Cerrado project of 2013, coordinated by the Ministry of the Environment (MMA). The time series of the enhanced vegetation index (EVI) obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua platforms (MOD/MYD13Q1 product) were used to estimate and describe the spatial and temporal variability of NPP through the Timesat software. The monthly ETR images were obtained from the MOD16A2 product and validated with observed ETR estimates from 27 meteorological stations of the National Institute of Meteorology (INMET). The average NPP of pastures grown in the Cerrado of Minas Gerais in the 12-year period was 5.98. The highest ETR rates were associated with higher NPP values. In general, cultivated pasture areas had a mean annual ETR of 690 mm, with 67% of the ETR occurring during the rainy season (October to April). Positive ET trends were found in more than 80% of the cultivated pastures. Mean value of WUE in the period considered was 0.08. According to these WUE data, 46% (5.48 million hectares) of the total cultivated pasturelands in the study area presented biological degradation processes. The Triângulo Mineiro/Alto Parnaíba and Norte de Minas mesoregions had the highest yields and the lowest losses of water to the atmosphere, therefore, the most productive pastures. The WUE technique allowed to evaluate the degradation conditions of cultivated pastures of the study area. The restoration of these degraded pastures can help increase beef production in this region, helping to reduce the rate of conversion of natural vegetation to new pasture areas

Crop growth and yield monitoring in smallholder agricultural systems:a multi-sensor data fusion approach

Smallholder agricultural systems are highly vulnerable to production risks posed by the intensification of extreme weather events such as drought and flooding, soil degradation, pests, lack of access to agricultural inputs, and political instability. Monitoring the spatial and temporal variability of crop growth and yield is crucial for farm management, national-level food security assessments, and famine early warning. However, agricultural monitoring is difficult in fragmented agricultural landscapes because of scarcity and uncertainty of data to capture small crop fields. Traditional pre- and post-harvest crop monitoring and yield estimation based on fieldwork is costly, slow, and can be unrepresentative of heterogeneous agricultural landscapes as found in smallholder systems in sub-Saharan Africa. Devising accurate and timely crop phenology detection and yield estimation methods can improve our understanding of the status of crop production and food security in these regions.Satellite-based Earth observation (EO) data plays a key role in monitoring the spatial and temporal variability of crop growth and yield over large areas. The small field sizes and variability in management practices in fragmented landscapes requires high spatial and high temporal resolution EO data. This thesis develops and demonstrates methods to investigate the spatiotemporal variability of crop phenology detection and yield estimation using Landsat and MODIS data fusion in smallholder agricultural systems in the Lake Tana sub-basin of Ethiopia. The overall aim is to further broaden the application of multi-sensor EO data for crop growth monitoring in smallholder agricultural systems.The thesis addressed two important aspects of crop monitoring applications of EO data: phenology detection and yield estimation. First, the ESTARFM data fusion workflow was modified based on local knowledge of crop calendars and land cover to improve crop phenology monitoring in fragmented agricultural landscapes. The approach minimized data fusion uncertainties in predicting temporal reflectance change of crops during the growing season and the reflectance value of fused data was comparable to the original Landsat image reserved for validation. The main sources of uncertainty in data fusion are the small field size and abrupt crop growth changes between the base andviiprediction dates due to flooding, weeding, fertiliser application, and harvesting. The improved data fusion approach allowed us to determine crop phenology and estimate LAI more accurately than both the standard ESTARFM data fusion method and when using MODIS data without fusion. We also calibrated and validated a dynamic threshold phenology detection method using maize and rice crop sowing and harvest date information. Crop-specific phenology determined from data fusion minimized the mismatch between EO-derived phenometrics and the actual crop calendar. The study concluded that accurate phenology detection and LAI estimation from Landsat–MODIS data fusion demonstrates the feasibility of crop growth monitoring using multi-sensor data fusion in fragmented and persistently cloudy agricultural landscapes.Subsequently, the validated data fusion and phenology detection methods were implemented to understand crop phenology trends from 2000 to 2020. These trends are often less understood in smallholder agricultural systems due to the lack of high spatial resolution data to distinguish crops from the surrounding natural vegetation. Trends based on Landsat–MODIS fusion were compared with those detected using MODIS alone to assess the contribution of data fusion to discern crop phenometric change. Landsat and MODIS fusion discerned crop and environment-specific trends in the magnitude and direction of crop phenology change. The results underlined the importance of high spatial and temporal resolution EO data to capture environment-specific crop phenology change, which has implications in designing adaptation and crop management practices in these regions.The second important aspect of the crop monitoring problem addressed in this thesis is improving crop yield estimation in smallholder agricultural systems. The large input requirements of crop models and lack of spatial information about the heterogeneous crop-growing environment and agronomic management practices are major challenges to the accurate estimation of crop yield. We assimilated leaf area index (LAI) and phenology information from Landsat–MODIS fusion in a crop model (simple algorithm for yield estimation: SAFY) to obtain reasonably reliable crop yield estimates. The SAFY model is sensitive to the spatial and temporal resolution of the calibration input LAI, phenology information, and the effective light use efficiency (ELUE) parameter, which needs accurate field level inputs during modelviiioptimization. Assimilating fused EO-based phenology information minimized model uncertainty and captured the large management and environmental variation in smallholder agricultural systems.In the final research chapter of the thesis, we analysed the contribution of assimilating LAI at different phenological stages. The frequency and timing of LAI observations influences the retrieval accuracy of the assimilating LAI in crop growth simulation models. The use of (optical) EO data to estimate LAI is constrained by limited repeat frequency and cloud cover, which can reduce yield estimation accuracy. We evaluated the relative contribution of EO observations at different crop growth stages for accurate calibration of crop model parameters. We found that LAI between jointing and grain filling has the highest contribution to SAFY yield estimation and that the distribution of LAI during the key development stages was more useful than the frequency of LAI to improve yield estimation. This information on the optimal timing of EO data assimilation is important to develop better in-season crop yield forecasting in smallholder systems

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Ecology of Savanna Ecosystems in Indonesia

Tropical savannas in South East Asia are important yet understudied ecosystems. In fact, the description of savanna is limited in the Indonesian Archipelago, a region which, based on climate alone, would be expected to have mostly forest. In this thesis, I compared and contrasted the vegetation characteristics of four savannas in the wetter part of the Indonesian archipelago (Java – Bali – Lombok) to understand how fire and grazing influence their dynamics, and searched for evidence of savanna origins, maintenance, invasion by exotic/woody species and possible ecosystem transitions. Vegetation surveys, remote sensing techniques and statistical models were used to spatially and temporally analyse the savanna community composition and the environmental variables and disturbances that influence their structure. there are distinct elevation gradients (along with related climatic factors such as temperature and precipitation), as well as fire regime gradients, linked to tropical savanna community composition across Java, Bali and Lombok Islands. These compositions are characterized by different sets of species, and where invasive alien species are becoming significant components of the ecosystems. Lack of prescribed fire and a range of invasive species threaten to convert savanna at Bali Barat and Alas Purwo into secondary forests or shrubland, whereas the presence of forest pioneer/edge species within the savanna at Rinjani suggests successional change from grassland to forest may occur in the absence of future fires (although the role of soil, topography and microclimate in maintaining grass dominance needs also to be further explored). Compared to the others studied, the savanna in Baluran National Park has characteristics of being relatively old and persistent rather than one being created and maintained via recent human conversion of forests. Overall in Indonesia, there is much less savanna compared to forests, hence it is expected that a greater percentage of savanna is burned. Using remote sensing analysis, I confirmed that approximately 2% of savanna/open vegetation had burned over a 14 year period, whereas only 0.8% of forest has burned across Indonesia. The extent and frequency of burning is mostly associated with annual Southern Oscillation Index (SOI). Most burning occurred in years when the SOI sustained negative SOI values, which generally means drier conditions across South East Asia. I also developed species distribution models for the main invasive alien species of the savanna ecosystems studied, Acacia nilotica, to establish its invasion potential, both locally in Baluran National Park and regionally in other parts of Indonesia. Acacia nilotica was different from the other invasive species studied, in that it is promoted by herbivory, and possibly also by fire. It appears that spatially, A. nilotica is rapidly advancing into the savanna of Baluran National Parks where it was observed that over fourteen years the savanna size has decreased (-1,361 ha), whilst the A. nilotica stand has increased in area (+ 1,886 ha). It was demonstrated that fire and grazing play an important role in this invasion. Results also show that global climate change is likely to increase the potential distribution of A. nilotica in Indonesia and the area at risk of invasion. By year 2045, A. nilotica has potential to spread across much of the eastern parts of Indonesia. As fire and grazing are common to savannas of eastern Indonesia, they are likely to facilitate its invasion into these areas. In summary, I have shown that savanna plant community in Indonesia is formed and maintained by interactions between climatic factors, fire regime and grazing. Invasive species were also present in the studied savannas such as Chromolaena odorata, and Lantana camara. These invasive species together with forest pioneer/edge specialist species (Ficus septica, Laportea stimulans, Melastoma polyanthum, Nauclea orientalis, Rubus rosifolius), may also be increasing in absence of fire and also may be leading to change of state from savanna to dense woody vegetation. Absence of fire seems to be changing structure and floristic of savanna vegetation which has implications for savanna species including rare fauna such as Jalak Bali/Bali Starling (Leucopsar rotschildi) and Javan Banteng/Wild Java cattle (Bos javanicus subsp. javanicus). Results from this thesis showed that Bali Starling range in Bali Island has shrunk to remaining small patches of fire-induced open shrub and savanna woodland found below an elevation of 150–175 m in the north-east part of peninsular Prapat Agung of Bali Barat National Park. The description of the savanna dynamics presented here provides further evidence of the complexity of the savanna ecosystem and its susceptibility to change as a result of changing fire regimes and invasion by invasive species. A greater understanding of the possible ecosystem processes driving the dynamics of the savannas will assist in the formulation of successful savanna management strategies at local and regional scales