A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices

Analyzing Vegetation Trends with Sensor Data from Earth Observation Satellites

Abstract This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots. First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period. Second, a systematic comparison between parametric and non-parametric techniques for analyzing trends in annual GIMMS-NDVI data is performed at fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change. Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006. Finally, a decision-making framework is presented to help analysts perform comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its research aim(s), showing that the framework can achieve the main goal of the study which is to advance the analysis of nonlinear changes in vegetation. The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology:A case study in Iraq

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R2 = 0.70 compared to the date of MODIS EVI (Avg R2 = 0.68) and a NPP (Avg R2 = 0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from − 20 to 20%, − 45 to 28% and − 48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach

Evaluación de la dinámica temporal de la materia orgánica en la cuenca de klyazma utilizando monitoreo remoto y qgis trends.earth

El artículo está dedicado al estudio de la dinámica de los procesos biológicos en los paisajes en los limites de la zona de captación. Se eligió como zona modelo la cuenca del río Klyazma (que esta entrando con un afluente de cuarto orden a la cuenca del Volga), que es una combinación bastante compleja de diferentes paisajes. El estudio se basó en datos de teledetección. Se eligieron como parámetros los indicadores de fito-productividad y de carbono del suelo. Se estableció que en los distintos paisajes los procesos biológicos difieren tanto en velocidad como en intensidad y responden de forma ambigua a los cambios en los parámetros climáticos y al cambio en el uso del suelo. Sin embargo, en general, la cuenca hidrográfica, como ecosistema único, mostró suficiente estabilidad en los procesos dinámicos. Esto indica que los ecosistemas naturales holísticos tienen internas propiedades compensatoria

Vegetation phenology from satellite imagery: the case of the Iberian Peninsula and Balearic Islands (2001-2017)

[EN] Phenological dynamics of vegetation is considered as an important biological indicator for understanding the functioning of terrestrial ecosystems. Land surface phenology (LSP), the study of vegetation phenology from time series of vegetation indices (IV), has provided a comprehensive overview of ecosystem dynamics. Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent study area for monitoring phenological dynamics of vegetation. The aim of this study is to analyse the spatial variability of the phenology of the vegetation of the Iberian Peninsula and Balearic Islands for the period 2001-2017. NDVI (Normalized Difference Vegetation Index) time series were generated from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters and with a composite period of 8 days. Atmospheric disturbances and noise were reduced using a Savitzky-Golay smoothing filter. Different phenological metrics or phenometrics were extracted using a threshold-based method. Results showed the existence of a different behaviour between spring and autumn phenophases in the Atlantic and Mediterranean biogeographic regions. The Mediterranean mountainous areas showed a similar phenological behaviour to the Atlantic vegetation. Biogeographic regions showed an internal variability, which may be derived from the different behaviour of land covers (e.g., natural vegetation vs. crops).[ES] La dinámica fenológica de la vegetación es considerada un importante indicador biológico para comprender el funcionamiento de los ecosistemas terrestres. La fenología de la superficie terrestre (Land Surface phenology; LSP), el estudio de la fenología de la vegetación a partir de series temporales de índices de vegetación (IV), ha proporcionado una visión integral de la dinámica de los ecosistemas. La península ibérica es una de las regiones con mayor diversidad de ecosistemas del continente europeo. Constituye, por lo tanto, una excelente área de estudio para la monitorización de la dinámica fenológica de la vegetación. El objetivo de este estudio es analizar la variabilidad espacial de la fenología de la vegetación de la península ibérica e islas Baleares para el periodo 2001-2017. Las series temporales de NDVI (Normalized Difference Vegetation Index) fueron generadas a partir del producto de reflectancia de superficie MOD09Q1 a una resolución espacial de 250 metros y con un periodo de compuesto de 8 días. Las perturbaciones atmosféricas y el ruido de las series temporales fueron atenuadas aplicando el algoritmo de suavizado de Savitzky-Golay. Las diferentes métricas fenológicas o fenométricas fueron extraídas usando un método basado en umbrales. Los resultados pusieron de manifiesto la existencia de un comportamiento diferenciado entre las fenofases de primavera y otoño en las regiones biogeográficas atlántica y mediterránea. Las zonas montañosas mediterráneas presentaron un comportamiento fenológico similar a la vegetación atlántica. La variabilidad interna de cada región biogeográfica también puede asociarse al diferente comportamiento entre cubiertas del suelo (e.g. vegetación natural vs. cultivos).El primer autor es un contratado pre-doctoral FPU financiado por el "Ministerio de Universidades" (Referencia FPU15/03758). Los autores agradecen el apoyo de los proyectos RTI2018-096561-A-I00 y US-1262552, financiados por el Ministerio de Ciencia e Innovación y la Agencia Estatal de Investigación / FEDER - Junta de Andalucía (Consejería de Economía y Conocimiento), respectivamente.Caparros-Santiago, J.; Rodríguez-Galiano, V. (2020). Estimación de la fenología de la vegetación a partir de imágenes de satélite: el caso de la península ibérica e islas Baleares (2001-2017). Revista de Teledetección. 0(57):25-36. https://doi.org/10.4995/raet.2020.13632OJS253605