521 research outputs found

    Indian earth observation programme towards societal benefits: GEOSS perspective

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    Indian Earth Observation (EO) Programme, since its inception has been applications driven and national development has been its main motivation. In order to meet the observational requirements of many societal benefit areas, a series of EO systems have been launched in both polar and geo synchronous orbits. Starting from Bhaskara, the first experimental EO satellite in 1979 to Cartosat-1 successfully launched in May 2005, a large number of sensors operating in optical and microwave spectral regions, providing data at resolutions ranging from 1 km to a meter have been built and flown. Data reception and processing facilities have been established not only in the country but also at various international ground stations. Remotely sensed data and its derived information have become an integral component of the National Natural Resources Management System (NNRMS), a unique concept evolved and established in the country. The paper discusses the evolution of IRS satellite systems, application programmes in different societal benefit areas and the road ahead. How it complements and supplements the international efforts in the context of Global Earth Observation System of Systems has also been indicated

    Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

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    Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

    Remote Sensing in Agriculture: State-of-the-Art

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    The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

    Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

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    Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters

    Role of earth observations for sustainable development: emerging trends

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    Sustainable development aims at optimal use of natural resources, protection and conservation of ecological systems, and improving economic efficiency. It tries to provide food, fuel, fiber and shelter for the ever increasing world population on sustainable basis. Ecosystems like agro, coastal, forest, freshwater and grasslands, and natural disasters are some of the ideal facets for examining the magnitude of the problems and the plausible solutions that need to be generated. Decision-making for sustainable development is a complex process and involves studying trade-offs that need to be made among conflicting goals of different sectors. The present paper examines the scope for Earth Observation (EO) contributions towards the above, and tries to profile the EO capabilities vis-a-vis the systems' information needs. It further identifies the gaps, which EO needs to fulfill towards meeting either the imaging or processing needs of various applications. Considering these, in the next 6-7 years, a host of spacecraft systems carrying different sensors have been planned across the globe including India. Endeavours of NASA, ESA, India, JAXA etc. and complementary data sharing initiatives by International Charter on Space and Major Disasters and the UN agencies are expected to enlarge the scope for increased role of EO data towards the cause of sustainable development

    Satellite-based monitoring of pasture degradation on the Tibetan Plateau: A multi-scale approach

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    The Tibetan Plateau has been entitled Third-Pole-Environment'' because of its outstanding importance for the global climate and the hydrological system of East and Southeast Asia. Its climatological and hydrological influences are strongly affected by the local vegetation which is supposed to be subject to ongoing degradation. The degradation of the Tibetan pastures was investigated on the local scale by numerous studies. However, because methods and scales substantially differed among the previous studies, the overall pattern of degradation on the Tibetan Plateau is hitherto unknown. Consequently, the aims of this thesis are to monitor recent changes in the grassland degradation on the Tibetan Plateau and to detect the underlying driving forces of the observed changes. Therefore, a comprehensive remote sensing based approach is developed. The new approach consists of three parts and incorporates different spatial and temporal scales: (i) the development and testing of an indicator system for pasture degradation on the local scale, (ii) the development of a MODIS-based product usable for degradation monitoring from the local to the plateau scale, and (iii) the application of the new product to delineate recent changes in the degradation status of the pastures on the Tibetan Plateau. The first part of the new approach comprised the test of the suitability of a new two-indicator system and its transferability to spaceborne data. The indicators were land-cover fractions (e.g.,~green vegetation, bare soil) derived from linear spectral unmixing and chlorophyll content. The latter was incorporated as a proxy for nutrient and water availability. It was estimated combining hyperspectral vegetation indices as predictors in partial least squares regression. The indicator system was established and tested on the local scale using a transect design and textit{in situ} measured data. The promising results revealed clear spatial patterns attributed to degradation, indicating that the combination of vegetation cover and chlorophyll content is a suitable indicator system for the detection of pasture degradation on local scales on the Tibetan Plateau. To delineate patterns of degradation changes on the plateau scale, the green plant coverage of the Tibetan pastures was derived in the second part. Therefore, an upscaling approach was developed. It is based on satellite data from high spatial resolution sensors on the local scale (WorldView-type) via medium resolution data (Landsat) to low resolution data on the plateau scale (MODIS). The different spatial resolutions involved in the methodology were incorporated to enable the cross-validation of the estimations in the new product against field observations (over 600 plots across the entire Tibetan Plateau). Four methods (linear spectral unmixing, spectral angle mapper, partial least squares regression, and support vector machine regression) were tested on their predictive performance for the estimation of plant cover and the method with the highest accuracy (support vector machine regression) was applied to 14 years of MODIS data to generate a new vegetation coverage product. In the third part, the changes in vegetation cover between the years 2000 and 2013 and their driving forces were investigated by comparing the trends in the new vegetation coverage product against climate variables (precipitation from tropical rainfall measuring mission and 2 m air temperature from ERA-Interim reanalysis data) on the entire Tibetan Plateau. Large areas in southern Qinghai were identified where vegetation cover increased as a result of positive precipitation trends. Thus, degradation did not proceed in these regions. Contrasting with this, large areas in the central and western parts of the Tibetan Autonomous Region were subject to an ongoing degradation. This degradation can be attributed to the coincidence of rising temperatures and anthropogenic induced increases in livestock numbers as a consequence of local land-use change. In those areas, the ongoing degradation influenced local precipitation patterns because sensible heat fluxes were accelerated above degraded pastures. In combination with advected moist air masses at higher atmospheric levels, the accelerated heat fluxes led to an intensification of local convective rainfall. The ongoing degradation detected by the new remote sensing approach in this thesis is alarming. The affected regions encompass the river systems of the Indus and Brahmaputra Rivers, where the ongoing degradation negatively affects the water storage capacities of the soils and enhances erosion. In combination with the feed-back mechanisms between plant coverage and the changed precipitation on the Tibetan Plateau, the reduced water storage capacity will exacerbate runoff extremes in the middle and lower reaches of those important river systems

    Simulation of site-specific irrigation control strategies with sparse input data

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    Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

    Air pollution and livestock production

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    The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

    Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

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    Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1 ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops

    Earth Observations and Integrative Models in Support of Food and Water Security

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    Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries
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