Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

Procjena pogoršanja ekološkog okruženja i vegetacije rudnika u Kini zbog prekomjernog vađenja ugljena važna je zbog osjetljivog ekološkog okruženja i niske temperature u hladnim i sušnim područjima. U ovom se istraživanju kao primjeri uzimaju rudnici Haizhou, Gulianhe i Huolinhe s otvorenim jamskim oknom te se predlaže metoda za procjenu njihovog ekološkog okruženja primjenom Landsat vremenske serije slika na temelju varijacija Normalized Difference Vegetation Index-a (NDVI) rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima. Prosječna NDVI vrijednost rudarskog područja izračunavala se svakog mjeseca primjenom podataka Landsat serije slika od 2000 do 2015. Područje nalazišta ugljena pod vegetacijom određeno je u skladu s graničnim vrijednostima NDVI pa su tako izrađeni grafikoni godišnjeg maksimalnog NDVI i područja pod vegetacijom. Prilagodili smo liniju trenda varijacije maksimalne vrijednosti NDVI i područja pod vegetacijom kako bi se smanjio učinak meteoroloških čimbenika na NDVI vrijednosti. Rezultati pokazuju da se poslije zatvaranja jame i čišćenja područja odlaganja, naglo, tijekom zadnjeg desetljeća, povećao NDVI rudnika s otvorenim jamskim otvorom i područja pod vegetacijom, a ekološko okruženje tih rudnika se očito poboljšalo. Rudarske aktivnosti su dovele do naglog opadanja godišnjeg maksimalnog NDVI i područja pod vegetacijom s trajno smrznutim slojem tla, a ekološko okruženje rudnika se nastavlja pogoršavati. Premda četverogodišnji prosječni NDVI ostaje nepromijenjen u dijelovima nalazišta ugljena koji se eksploatiraju, a nemaju trajno smrznuti sloj tla, područje ugljenokopa pod vegetacijom se linearno smanjuje, ukazujući na činjenicu da se ekološko okruženje ugljenokopa pogoršava. Sa stajališta zaštite ekološkog okruženja, rezultati ovog istraživanja čine osnovu za donošenje odluke o otvaranju velikih rudnika s otvorenim jamskim oknom u hladnim i sušnim područjima.Evaluating the deterioration of ecological environment and vegetation of coalfields caused by China’s large-scale coal mining activities is important because of the fragile ecological environment and low temperature in cold and arid areas. This study takes the open coal pits of Haizhou, Gulianhe, and Huolinhe as examples and proposes a method for evaluating their ecological environment using Landsat time series images based on the Normalized Difference Vegetation Index (NDVI) variations of open-pit coalfields in cold and arid zones. The average NDVI value of the mining area each month was calculated using Landsat image data from 2000 to 2015. The vegetation cover area in the coalfields was extracted according to the NDVI threshold, and the scatter plots of the annual maximum NDVI and vegetation cover area were drawn. We fitted the variation trend line of maximum NDVI value and vegetation cover area to reduce the effect of meteorological factors on NDVI values. Results show that after the closure of open pit and reclamation of dump area, the NDVI of open-pit coalfields and vegetation cover area have been increasing rapidly over the last decade, and the ecological environment of these coalfields has obviously improved. The coal mining activities have led to the rapid decline of annual maximum NDVI and vegetation cover area of the coalfields in permafrost zones, and the ecological environment of coalfields continues to deteriorate. Although the quarterly average NDVI remains unchanged in non-permafrost mining coalfields under coal exploitation, the vegetation cover area in the coalfields decreases linearly, indicating that the ecological environment of the coalfields tends to deteriorate. From an ecological environment protection perspective, the results of this study provide a basis for decision making in constructing large-scale open pits in cold and arid zones

Monitoring the Land Cover Change in the Historic Range of Cambarus veteranus in West Virginia Using a 1973-2013 Landsat Time Series

The crayfish Cambarus veteranus is near extinction in its historic range of the Upper Guyandotte River watershed. The biggest threats to C. veteranus are mining and road construction. Mining has been a continuous activity in the southern coalfields where the crayfish has historically been found, yet little is known about how much land cover change the practice has done to the region. Crayfish act as important organisms within aquatic ecosystems and without them, those systems are often degraded. Quantifying the change in land cover is important to understanding threats to C. veteranus for future protection of the crayfish and its habitat. Using twelve Landsat satellite images from 1973-2013, I performed a supervised land cover classification to track land cover change within the Upper Guyandotte River watershed. There was an overall 5.5% change in land cover with a significant decreasing trend in forested area over time. In addition to overall land cover changing, three, out of seven, subwatersheds where C. veteranus was historically found saw significant decreasing trends in forested area as well. The last known location of C. veteranus is within one of those three watersheds. This increased disturbance from mining likely explains the near extinction of Cambarus veteranus. Without further protection and monitoring the land cover, the crayfish is likely to go extinct within its native West Virginia range

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) "Index-then-Blend" (IB); and (ii) "Blend-then-Index" (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm

Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau

The increasingly intensive and extensive coal mining activities on the Loess Plateau pose a threat to the fragile local ecosystems. Quantifying the effects of coal mining activities on environmental conditions is of great interest for restoring and managing the local ecosystems and resources. This paper generates dense NDVI (Normalized Difference Vegetation Index) time series between 2000 and 2011 at a spatial resolution of 30 m by blending Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and further evaluates its capability for mapping vegetation trends around a typical coalfield on the Loss Plateau. Synthetic NDVI images were generated using (1) STARFM-generated NIR (near infrared) and red band reflectance data (scheme 1) and (2) Landsat and MODIS NDVI images directly as inputs for STARFM (scheme 2). By comparing the synthetic NDVI images with the corresponding Landsat NDVI, we found that scheme 2 consistently generated better results (0.70 < R2 < 0.76) than scheme 1 (0.56 < R2 < 0.70) in this study area. Trend analysis was then performed with the synthetic dense NDVI time series and the annual maximum NDVI (NDVImax) time series. The accuracy of these trends was evaluated by comparing to those from the corresponding MODIS time series, and it was concluded that both the trends from synthetic/MODIS NDVI dense time series and synthetic/MODIS NDVImax time series (2000–2011) were highly consistent. Compared to trends from MODIS time series, trends from synthetic time series are better able to capture fine scale vegetation changes. STARFM-generated synthetic NDVI time series could be used to quantify the effects of mining activities on vegetation, but the test areas should be selected with caution, as the trends derived from synthetic and MODIS time series may be significantly different in some areas

Hodnocení fenologie vegetace pomocí časových řad dat Sentinel-2

Cílem této práce je vyhodnotit detekci fenologických fází vegetace na základě fenometrických parametrů podle archivních dat Sentinel-2 ve vybraných oblastech v období 2018-2020. V první části práce je uveden literární přehled relevantních publikací, na který navazuje popis navržené metodiky. Poté jsou uvedeny výsledky s grafickými materiály a popisem pro jednotlivé sledované lokality. V závěrečné části práce jsou diskutovány výhody a nevýhody vytvořeného algoritmu, na které navazují návrhy na budoucí výzkum a zlepšení. Vyvinutý algoritmus se skládá ze 2 částí. Odmaskování oblačných pixelů a generování na časové řadě vegetačních indexů se provádí v prostředí GEE. Analýza časových řad a detekce SOS a EOS a statistická analýza se provádí v prostředí R. Studované plochy 20 x 20 m reprezentují různé druhy trvalé vegetace na celém území České republiky. Pro hodnocení detekce fenofází jsou zvoleny hodnoty NDVI, RENDVI, NDRE, NDMI a MCARI. Asymetrická Gaussova funkce a Dvojitá logistická funkce jsou aplikovany na časové řady jednotlivých vegetačních období v každé testované lokalitě, fenologické parametry jsou odvozeny na základě prahových hodnot nebo derivací. Výsledky jsou ověřeny na základě in-situ dat poskytnutých ČHMÚ. NDMI vykázal nejvyšší přesnost při detekci SOS při použití Asymetrické Gaussovy...This work aims to evaluate the detection of phenological phases of vegetation based on phenometric parameters according to archival Sentinel-2 data in the selected areas over the period 2018-2020. The first part of the work describes literature review of the relevant publications, which is followed by the description of the suggested methodology. Then, there are the results with the graphic material and description for each monitored site. In the final part of the work, advantages and disadvantages of the developed algorithm are discussed followed up by suggestions for future research and improvement. The developed algorithm consists of two parts. Masking out cloudy and cloud shadow pixels and generation on the vegetation indices time series is done in the GEE platform. The time series analysis and detection of SOS and EOS as well as statistical analysis are done in the R environment. The study areas of size 20 x 20 m represent different species of perennial vegetation across the Czech Republic. For the assessment of the phenophases detection are selected NDVI, RENDVI, NDRE, NDMI and MCARI. The Asymmetric Gaussian function and Double Logistic function are fitted to the time series of each vegetation season in each tested site, the phenology metrics are derived based on threshold or derivatives...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

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

Series multitemporales Landsat/MODIS en el análisis de áreas quemadas en ambientes de sabana tropical de la Amazonia Meridional brasileña

Globalmente, el fuego es uno de los principales elementos en la dinámica de los ecosistemas terrestres, siendo su seguimiento y análisis relevante para la comprensión de los procesos ecológicos e impactos humanos a diferentes escalas. En Brasil, las áreas más frecuentemente afectadas por el fuego se sitúan en las zonas de transición entre las sabanas tropicales y la Amazonia, vinculadas a fuerte influencia antrópica que altera los regímenes naturales de fuego.La presente tesis tiene como objetivo analizar los patrones espacio-temporales y la dinámica de regeneración vegetal de áreas afectadas por el fuego en ambientes de sabana tropical de la Amazonia Meridional brasileña, centrando el análisis en un área que constituye el mayor enclave de sabana tropical del sur de la Amazonia, los Campos Amazônicos. Con este fin, se explora el uso de las series multitemporales derivadas de los sensores/satélites Landsat y MODIS, buscando combinar el potencial de cada serie en la generación y análisis de informaciones anuales/estacionales que contribuyan a la interpretación de la incidencia del fuego y sus repercusiones en la dinámica de estos paisajes.La generación de una serie anual/estacional ha permitido cartografiar un total de 1.03 millones de hectáreas afectadas por el fuego en el área de estudio (prácticamente 2.5 veces su área total), en el período comprendido entre 2000 y 2016. Se ha identificado una fuerte influencia de la estacionalidad en los subtotales anuales, donde las cicatrices de fuego de los meses más extremos de sequía representan el 86% del total de área quemada y el 32% del número de incendios. Estos incendios afectan proporcionalmente más a las superficies de mayor densidad de vegetación leñosa, a diferencia de lo que sucede al principio o fuera del período de sequía.La base multitemporal generada también ha sido utilizada como referencia para evaluar las últimas generaciones de los productos de área quemada de MODIS en ecosistemas de sabana tropical. Se han identificado las nuevas prestaciones del último producto (MCD64A1 v006), asociadas principalmente a la disminución significativa de los errores de omisión, al tiempo que se mantienen los bajos niveles de comisión ya logrados con las versiones anteriores.Se comprueba que la fusión de imágenes Landsat y MODIS a través del método Flexible Spatiotemporal Data Fusion constituye una buena alternativa para subsanar problemas de falta de información en series multitemporales de media/alta resolución espacial en ambientes de sabana tropical afectados por el fuego. La combinación de la serie multitemporal Landsat disponible y el uso del fusionado Landsat-MODIS ha permitido generar con éxito una serie de media/alta resolución espacial con ocho composiciones anuales, para el período de 2009-2016, en áreas del enclave de los Campos Amazônicos. El análisis de las trayectorias utilizando índices espectrales, muestra descensos abruptos de sus valores en respuesta a la acción del fuego. Estos efectos son perceptibles en las trayectorias durante los dos años siguientes al fuego; a partir del tercer año, los valores de los índices se asemejan a las condiciones pre-fuego. Estos resultados han sido verificados con datos de campo, a partir de los cuales también se ha podido cuantificar una mayor acumulación de biomasa seca en aquellas parcelas en las que ha transcurrido más tiempo sin ser afectadas por el fuego.En definitiva, en el contexto de las series multitemporales de productos de teledetección de media/alta resolución espacial, se han aplicado metodologías y generado información que han permitido avanzar en el seguimiento de la incidencia del fuego en ambientes de sabana tropical de la Amazonia Meridional. Los resultados refuerzan, espacial y estadísticamente, importantes argumentos respecto a la estacionalidad de las quemas en estos paisajes, así como permiten progresar en la comprensión del proceso de regeneración vegetal post-fuego en estas áreas.<br /