Распознавание залежных земель на основе сезонных значений вегетационного индекса NDVI

Проведен дискриминантный анализ для распознавания залежных земель на основе их спектрально-отражательных характеристик. Предложена методика автоматизированного выявления залежей среди пашни, основанная на дискриминантом анализе сезонных значений вегетационного индекса NDV

Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

Multi-scale targeting of land degradation in northern Uzbekistan using satellite remote sensing

Advancing land degradation (LD) in the irrigated agro-ecosystems of Uzbekistan hinders sustainable development of this predominantly agricultural country. Until now, only sparse and out-of-date information on current land conditions of the irrigated cropland has been available. An improved understanding of this phenomenon as well as operational tools for LD monitoring is therefore a pre-requisite for multi-scale targeting of land rehabilitation practices and sustainable land management. This research aimed to enhance spatial knowledge on the cropland degradation in the irrigated agro-ecosystems in northern Uzbekistan to support policy interventions on land rehabilitation measures. At the regional level, the study combines linear trend analysis, spatial relational analysis, and logistic regression modeling to expose the LD trend and to analyze the causes. Time series of 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), summed over the growing seasons of 2000-2010, were used to determine areas with an apparent negative vegetation trend; this was interpreted as an indicator of LD. The assessment revealed a significant decline in cropland productivity across 23% (94,835 ha) of the arable area. The results of the logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table, land-use intensity, low soil quality, slope, and salinity of the groundwater. To quantify the extent of the cropland degradation at the local level, this research combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping based on multitemporal Landsat TM images from 1998 and 2009. Spatial distribution of fields with decreased vegetation cover is mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison of the Landsat-based map with the LD trend map yielded an overall agreement of 93%. The proposed methodological approach is a useful supplement to the commonly applied trend analysis for detecting LD in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. To contribute to land rehabilitation options, a GIS-based multi-criteria decision-making approach is elaborated for assessing suitability of degraded irrigated cropland for establishing Elaeagnus angustifolia L. plantations while considering the specific environmental setting of the irrigated agro-ecosystems. The approach utilizes expert knowledge, fuzzy logic, and weighted linear combination to produce a suitability map for the degraded irrigated land. The results reveal that degraded cropland has higher than average suitability potential for afforestation with E. angustifolia. The assessment allows improved understanding of the spatial variability of suitability of degraded irrigated cropland for E. angustifolia and, subsequently, for better-informed spatial planning decisions on land restoration. The results of this research can serve as decision-making support for agricultural planners and policy makers, and can also be used for operational monitoring of cropland degradation in irrigated lowlands in northern Uzbekistan. The elaborated approach can also serve as a basis for LD assessments in similar irrigated agro-ecosystems in Central Asia and elsewhere.Multisclare Bewertung der Landdegradation in Nord-Uzbekistan unter der Verwendung von Satellitenfernerkundung Die zunehmende Landdegradation (LD) in den bewässerten Agrarökosystemen in Usbekistan behindert die nachhaltige Entwicklung dieses vorwiegend landwirtschaftlich geprägten Landes. Bis heute sind nur wenige und veraltete Informationen über die aktuellen Bodenbedingungen der bewässerten Anbauflächen verfügbar. Ein besseres Verständnis dieses Phänomens sowie operationelle Werkzeuge für LD-Monitoring sind daher Voraussetzung für ein nachhaltiges Landmanagement sowie für Landrehabilitationsmaßnahmen. Ziel dieser Studie war es, das räumliche Verständnis der Degradierung von Anbaugebieten in den bewässerten Agrarökosystemsn des nördlichen Usbekistans zu verbessern, um staatliche Interventionen in Bezug auf Landrehabilitationsmaßnahmen zu unterstützen Auf der regionalen Ebene kombiniert die Studie lineare Trendanalyse, räumliche relationale Analyse sowie logistischer Regressionsmodellierung, um den LD-Trend darzustellen und Gründe zu analysieren. Zeitreihen von 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) Bildern wurden für den Zeitraum der Anbauperioden zwischen 2000-2010 untersucht, um Bereiche mit einem offensichtlich negativen Vegetationstrend zu ermitteln. Dieser negative Trend kann als Indikator für LD interpretiert werden. Die Untersuchung ergab eine signifikante Abnahme der Bodenproduktivität auf 23% (94,835 ha) der Anbaufläche. Zudem deuten die Ergebnisse der logistischen Modellierung darauf hin, dass das räumliche Muster des beobachteten Trends überwiegend mit der Höhe des Grundwasserspiegels, der Landnutzungsintensität, der geringen Bodenqualität, der Hangneigung sowie der Grundwasserversalzung zusammenhängt. Um das Ausmaß der Degradation der Anbauflächen auf der lokalen Ebene zu quantifizieren, kombiniert diese Studie objektbasierte Erkennung von Veränderungen und spektrale Mischungsanalyse für die Abnahme der Vegetationsbedeckung auf der Grundlage von multitemporalen Landsat-TM-Bildern im Zeitraum von 1998 bis 2009. Die räumliche Verteilung der Felder mit abnehmender Vegetationsbedeckung hängt überwiegend mit verlassenen Anbauflächen sowie mit nährstoffarmen Böden in den Randbereichen des Bewässerungssystems und an den Grenzen zu natürlichen Sandwüsten zusammen. Ein Vergleich mit der Karte des LD-Trends ergab insgesamt eine Übereinstimmung von 93%. Der vorgeschlagene Ansatz ist eine nützliche Ergänzung zu der häufig angewendeten Trendanalyse für die Ermittlung von LD in Regionen, für die keine Satellitenbildzeitreihen mit hoher Auflösung verfügbar sind. Als Beitrag zu Landrehabilitationsmöglichkeiten, wird ein GIS-basierter Multi-Kriterien-Ansatz zur Einschätzung der Eignung von degradierten bewässerten Anbauflächen für Elaeagnus angustifolia L. Plantagen beschrieben, der gleichzeitig die spezifischen Umweltbedingungen der bewässerten Agrarökosysteme berücksichtigt. Dieser Ansatz beinhaltet Expertenwissen, Fuzzy-Logik und gewichtete lineare Kombination, um eine Eignungskarte für die bewässerten degradierten Anbauflächen herzustellen. Die Ergebnisse zeigen, dass diese Flächen ein überdurchschnittliches Eignungspotenzial für die Aufforstung mit E. angustifolia aufweisen. Diese Studie trägt zu einem verbesserten Verständnis der räumlichen Variabilität der Eignung von solchen Flächen für E. angustifolia bei. Die Ergebnisse dieser Studie können als Entscheidungshilfe für landwirtschaftliche Planer und politische Entscheidungsträger sowie für verbesserte Landrehabilitationsmaßnahmen und operationelles Monitoring der Degradation von Anbauflächen im nördlichen Usbekistan eingesetzt werden. Zudem kann der beschriebene Ansatz als Grundlage für LD-Untersuchungen in ähnlichen bewässerten Agrarökosystemen in Zentralasien und anderswo dienen

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio

Numerical modelling of moisture motion in heterogeneous soils using 1D-MIRBF method

In the present paper, we develop an efficient and accurate numerical approach based on one-dimensional-moving integrated radial basis function (1D-MIRBF) and fully implicit modified Picard method for simulating fluid movement in heterogeneous soils governed by the highly non-linear Richards equation. The major advantages of the proposed 1D-MIRBF method include (i) a banded sparse system matrix that helps reduce the computational cost; (ii) the Kronecker Delta property of the constructed shape functions, which helps impose the essential boundary conditions in an exact manner; and (iii) high accuracy and fast convergence rate owing to the use of the IRBF approximation. The performance of the present method is demonstrated through several 1--D and 2--D soil infiltration problems. Numerical results obtained are in agreement with other published results in the literature. This solver for moisture motion in soils will be incorporated into a surface-water-flow solver to handle the surface irrigation problem

Uso de séries temporais do sensor MODIS para identificar diferentes culturas agrícolas

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2018.A presente pesquisa objetiva identificar culturas de grãos a partir de séries temporais NDVI MODIS. As culturas agrícolas e regiões analisadas foram: (a) soja, milho e algodão no Estado do Mato Grosso na safra de 2013/2014; (b) trigo no Estado do Rio Grande do Sul; (c) e cultura do arroz no Estado de Santa Catarina. A tese está estruturada em 5 (cinco) capítulos, onde os capítulos de desenvolvimento (2, 3 e 4) foram escritos no formato de artigos científicos. No processamento digital de imagem todas as análises consideraram as seguintes etapas: (a) aquisição das imagens MODIS; (b) tratamento dos ruídos usando o filtro Savitzky- Golay; (c) classificação; e (d) análise de acurácia. A principal diferença metodológica foi a etapa de classificação que utilizou duas abordagens: (a) classificação contínua do terreno considerando as diferentes produções agrícolas (soja, milho e algodão) e os tipos de vegetação a partir de dois métodos de aprendizagem de máquina (Support Vector Machines e Redes Neurais de retro-propagação); e (b) detecção de uma única cultura de pequenos agricultores (arroz em Santa Catarina e trigo no Rio Grande do Sul) usando o método do vizinho mais próximo (caso específico do método K-NN). A primeira abordagem usando classificação contínua do terreno considerou as seguintes assinaturas temporais NDVI: formação florestal, cerrado, pastagem, sistema único de cultivo anual (soja, milho e algodão), sistema duplo de cultivo (soja/milho e soja/algodão) e pivô central (sistema triplo de cultivo). Na classificação foram testados 378 modelos de redes neurais com variações dos parâmetros de entrada e 8 modelos SVM usando diferentes funções Kernel. O índice Kappa mostrou que os melhores modelos da Rede Neural (0,77) e SVM (0,75) foram estatisticamente equivalentes pelo teste McNemar. A classificação baseada no vizinho mais próximo foi constituida de duas fases: (a) geração de imagens métricas (distância Euclidiana e similaridade do cosseno); e (b) definição do melhor valor de corte para caracterizar a máscara da cultura agrícola. Os resultados mostraram diferentes perfis temporais tanto no trigo como no arroz devido às variações do calendário agrícola da região. Nas duas classificações (trigo e arroz), os resultados usando as duas métricas foram estatisticamente equivalentes pelo teste McNemar. Na análise do trigo, a distância Euclidiana obteve um índice Kappa de 0,75 e a semelhança do cosseno um índice Kappa de 0,74. Na análise do arroz a distância Euclidiana obteve um índice Kappa de 0,73 e a semelhança do cosseno um índice Kappa de 0,72. As metodologias descritas demonstram uma grande potencial para o cálculo das áreas de produção agrícola, podendo auxiliar os órgãos federais para o planejamento regional e segurança alimentar.The present research aims to identify grain crops from NDVI MODIS time series. The agricultural crops and the analyzed regions were: (a) soybean, corn and cotton in Mato Grosso State at 2013/14 growing season; (b) wheat in the State of Rio Grande do Sul; (c) and rice in the State of Santa Catarina. The thesis is structured in 5 (five) chapters, where the development chapters (2, 3 and 4) were written in the format of scientific articles. In digital image processing, all analyzes considered the following steps: (a) acquisition of MODIS images; (b) noise treatment using the Savitzky-Golay filter; (c) classification; and (d) accuracy analysis. The main methodological difference was the classification stage that used two approaches: (a) continuous land classification considering the different agricultural production (soybean, corn and cotton) and vegetation types from two methods of machine learning ( Support Vector Machines and Retro-propagation Neural Networks); and (b) detection of a single crop of small farmers (rice in the Santa Catarina and wheat in the Rio Grande do Sul) using the nearest neighbor method (specific case of the K-NN method). The first approach using continuous land classification considered the following NDVI temporal signatures: forest formation, cerrado, pasture, single annual cropping system (soybean, corn and cotton), double cropping system (soybean / corn and soybean / cotton) and pivot (triple cropping system). In the classification were tested 378 models of neural networks with different variations in input parameters and 8 SVM models using different Kernel functions. The Kappa index showed that the best models of the Neural Network (0.77) and SVM (0.75) were statistically equivalent by the McNemar test. The classification based on the nearest neighbor was constituted of two phases: (a) elaboration of metric images (Euclidean distance and similarity of the cosine); and (b) definition of the best threshold value to characterize the agricultural crop mask. The results showed different temporal profiles in both wheat and rice due to variations in the region's agricultural calendar. In both classifications (wheat and rice), the results using the two metrics were statistically equivalent by the McNemar test. In wheat analysis, the Euclidean distance obtained a Kappa index of 0.75 and the cosine similarity a Kappa index of 0.74. In rice analysis, the Euclidian distance obtained a Kappa index of 0.73 and the cosine similarity a Kappa index of 0.72. The methodologies showed a promising potential to determine the areas of crop production and could be very useful for federal agencies for regional planning and food security programs

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl