335 research outputs found

    Remote sensing of opium poppy cultivation in Afghanistan

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    This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies

    ACCURACIES, ERRORS, AND UNCERTAINTIES OF GLOBAL CROPLAND PRODUCTS

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    ABSTRACT ACCURACIES, ERRORS, AND UNCERTAINTIES OF GLOBAL CROPLAND PRODUCTS By Kamini Yadav University of New Hampshire, May 2019 Global cropland products are continuously being produced at different spatial resolutions using remotely sensed satellite imagery. Recently, with our increased accessibility to higher computing processing, three different cropland extent maps have been developed as a part of Global Food Security-Support Analysis Data (GFSAD) project at three spatial resolutions (i.e., GFSAD1km, GFSAD250m, and GFSAD30m). All cropland maps should be assessed for their accuracy, errors, and uncertainty for various agriculture monitoring applications. However, in previous assessment efforts appropriate assessment strategies have not always been applied and many have reported only a single accuracy measure for the entire world. This research was divided into four components to provide more attention and focus on the accuracy assessment of large area cropland products. First, a valid assessment of cropland extent maps was performed addressing different strategies, issues, and constraints depending upon various conditions related to the cropland distribution, proportion, and pattern present in each continent. This research focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to the United States), Africa and Australia. Continent-specific sampling strategies and accuracy assessments were performed within homogenous regions (i.e., strata) of different continents to ensure that an appropriate reference data set was collected to generate rigorous and valid accuracy results indicative of the actual cropland proportion. Second, all the three different GFSAD cropland extent maps were assessed using appropriate sampling and collection of a cropland reference data based on the cropland distribution and proportion for different regions in the entire world. In addition to the accuracy assessment, the cropland extent maps developed at the three spatial resolutions were compared to investigate the differences among them and provide guidance for users to select the appropriate resolution given different agriculture field sizes. The comparison of three different GFSAD cropland extent maps was performed based on the similarity of the cropland area proportion (CAP) and landscape clumping at different spatial resolutions to provide specific recommendations for when to apply these maps in different agriculture field sizes. Third, an issue was discovered with the accuracy assessment of 30m global cropland extent map (i.e., GFSAD30m) in that insufficient samples were collected resulting in an ineffective assessment when the cropland map class was rare as occurred in some regions around the world. This research evaluated the sampling designs for different cropland regions to achieve sufficient samples and effective accuracy of rare cropland map class by comparing the distribution, allocation of samples and accuracy measures. The evaluation of sampling designs demonstrated that the cropland regions of \u3c15% CAP must be sampled with an appropriate stratified sampling combined with a predetermined minimum sample size for each map class. Finally, the accuracy assessment of all thematic maps (e.g., crop type maps) needs sufficient reference data to conduct a valid assessment. The availability of reference data is a severely limiting factor over large geographic region because of the time, effort, cost, and accessibility in different parts of the world. The objectives of this research were to augment and extend the limited availability of crop type reference data using non-ground-based sources of crop type information for creating and assessing large area crop type maps. There is the potential to either interpret the photographs available from Google Street View (GSV) or classify High Resolution Imagery (HRI) using a phenology-based classification approach to generate additional reference data within similar agriculture ecological zones (AEZs) based on the crop characteristics, their types, and growing season. These two methods of augmenting and extending crop type reference data were developed for the United States (US) where high-quality crop type reference data already exist so that the methods could be effectively and efficiently tested. This research described a tale of three continents providing recommendations to adapt accuracy assessment strategies and methodologies for assessing global cropland extent maps. Based on these results, the assessment and comparison of different resolution GFSAD cropland extent maps were performed to provide specific recommendations for when to apply each of the maps for agriculture monitoring based on the agriculture field sizes. When assessing the cropland extent maps, different sampling strategies perform differently in the various cropland proportion regions and therefore, must be selected according to the cropland extent maps to be assessed. Finally, this research concluded that the limited crop type reference data can be effectively extended using a phenology-based classification approach and is more efficient than the interpretation of photographs collected from GSV

    Remote sensing of agricultural crops and soils

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    Research results and accomplishments of sixteen tasks in the following areas are described: (1) corn and soybean scene radiation research; (2) soil moisture research; (3) sampling and aggregation research; (4) pattern recognition and image registration research; and (5) computer and data base services

    [Photointerpretation of Skylab Imagery]

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    There are no author-identified significant results in this report

    Briefing Materials for Technical Presentations, Volume A: The LACIE Symposium

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    Tables, charts, and outlines of various segments within the Large Area Crop Inventory Experiment are presented. Experiment design, system implementation and operations, and data processing system design were considered

    Landcover and crop type classification with intra-annual times series of sentinel-2 and machine learning at central Portugal

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    Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesLand cover and crop type mapping have benefited from a daily revisiting period of sensors such as MODIS, SPOT-VGT, NOAA-AVHRR that contains long time-series archive. However, they have low accuracy in an Area of Interest (ROI) due to their coarse spatial resolution (i.e., pixel size > 250m). The Copernicus Sentinel-2 mission from the European Spatial Agency (ESA) provides free data access for Sentinel 2-A(S2a) and B (S2b). This satellite constellation guarantees a high temporal (5-day revisit cycle) and high spatial resolution (10m), allowing frequent updates on land cover products through supervised classification. Nevertheless, this requires training samples that are traditionally collected manually via fieldwork or image interpretation. This thesis aims to implement an automatic workflow to classify land cover and crop types at 10m resolution in central Portugal using existing databases, intra-annual time series of S2a and S2b, and Random Forest, a supervised machine learning algorithm. The agricultural classes such as temporary and permanent crops as well as agricultural grasslands were extracted from the Portuguese Land Parcel Identification System (LPIS) of the Instituto de Financiamento da Agricultura e Pescas (IFAP); land cover classes like urban, forest and water were trained from the Carta de Ocupação do Solo (COS) that is the national Land Use and Land Cover (LULC) map of Portugal; and lastly, the burned areas are identified from the corresponding national map of the Instituto da Conservação da Natureza e das Florestas (ICNF). Also, a set of preprocessing steps were defined based on the implementation of ancillary data allowing to avoid the inclusion of mislabeled pixels to the classifier. Mislabeling of pixels can occur due to errors in digitalization, generalization, and differences in the Minimum Mapping Unit (MMU) between datasets. An inner buffer was applied to all datasets to reduce border overlap among classes; the mask from the ICNF was applied to remove burned areas, and NDVI rule based on Landsat 8 allowed to erase recent clear-cuts in the forest. Also, the Copernicus High-Resolution Layers (HRL) datasets from 2015 (latest available), namely Dominant Leaf Type (DLT) and Tree Cover Density (TCD) are used to distinguish between forest with more than 60% coverage (coniferous and broadleaf) such as Holm Oak and Stone Pine and between 10 and 60% (coniferous) for instance Open Maritime Pine. Next, temporal gap-filled monthly composites were created for the agricultural period in Portugal, ranging from October 2017 till September 2018. The composites provided data free of missing values in opposition to single date acquisition images. Finally, a pixel-based approach classification was carried out in the “Tejo and Sado” region of Portugal using Random Forest (RF). The resulting map achieves a 76% overall accuracy for 31 classes (17 land cover and 14 crop types). The RF algorithm captured the most relevant features for the classification from the cloud-free composites, mainly during the spring and summer and in the bands on the Red Edge, NIR and SWIR. Overall, the classification was more successful on the irrigated temporary crops whereas the grasslands presented the most complexity to classify as they were confused with other rainfed crops and burned areas

    Operational progression of digital soil assessment for agricultural growth in Tasmania, Australia

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    Tasmania, Australia, is currently undergoing a period of agricultural expansion through the development of new irrigation schemes across the State, primarily to stimulate the economy and ensure future food security. ‘Operational Progression of Digital Soil Assessment (DSA) for Agricultural Growth in Tasmania, Australia’ presents the adaptation and operationalisation of quantitative approaches for regional land evaluation within these schemes, specifically applied Digital Soil Mapping (DSM) to inform a land suitability evaluation for 20 different agricultural crops, and ultimately a spatial indication of the State’s agricultural versatility and capital. DSM had not previously been applied or tested in Tasmania; the research examines and validates DSM approaches with respect to the State’s unique and complex soils and biophysical interactions with climate and terrain, and how these apply to various agricultural land uses. The thesis is a major contribution to the methodology and development of one of the first major operational DSA programs in Australia, and forms a framework for this type of DSM approach to be used in future operational land evaluation elsewhere

    Evaluation of Tidal Fresh Forest Distributions and Tropical Storm Impacts Using Sentinel-2 MSI Imagery

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    Situated in the transitional zone between non-tidal forests upstream and tidal fresh marshes downstream, tidal fresh forests occupy a unique and increasingly precarious habitat. The threat of intensifying anthropogenic climate change, compounded by the effects of historical logging and drainage alterations, could reduce the extent of this valuable ecosystem. The overall goals of this project were to identify forest communities present in the Altamaha tidal fresh forest; develop satellite imagery-based classifications of tidal fresh forest and tidal marsh vegetation along the Altamaha River, Georgia; and to quantify changes in vegetation distribution in the aftermath of hurricanes Matthew and Irma. Based on vegetation data gathered during our field survey, we identified at least eight distinct forest communities with hierarchical clustering methods. Using Sentinel-2 Multispectral Imager (MSI) satellite imagery and a balanced random forest classifier, we mapped land cover for six anniversary images from 2016 to 2021 to examine changes in vegetation distributions. Overall classification accuracies ranged from 80 to 86%, and we were able to accurately discriminate between several classes at the species level. Over our six year study period we did not observe any substantial changes in land cover, including the forest-marsh transition, suggesting resilience to tropical weather impacts. We postulate that this stasis may be due to the large volume of freshwater delivered by the Altamaha River and the extensive tidal marshes of the Altamaha estuary, which protect freshwater wetlands from the short-term effects of saltwater intrusion by reducing salinity and buffering them from acute pulse events such as hurricane storm surges

    Urban Expansion, Land Use Land Cover Change and Human Impacts: A Case Study of Rawalpindi

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    Urbanization in Pakistan has increased rapidly from 25% in 1972 to 42% in 2012. Peripheral zones are being pushed by urbanization much beyond their previous extents. Moreover, dispersed developments along the highways/motorways and unplanned expansion of existing urban centres is instigating a substantial loss of vegetation and open spaces. This research is an effort to analyse the relationship between urban expansion and land use/cover change using a combination of remote sensing, census and field data. Rawalpindi has been chosen as a study area because of its rapidly changing population density and land cover over the last few decades, and availability of satellite and census data. Landsat MSS and TM images of 1972, 1979, 1998 and 2010 which are compatible with the 1972, 1981, 1998 and 2012 Census of Pakistan dates were classified using the Maximum Likelihood classifier. The results of the assessment of classification accuracy yielded an overall accuracy of 75.16%, 72.5%, for Landsat MSS 1972, 1979 images and 84.5% and 87.1% for Landsat TM 1998 and 2010 images. Results reveal that the built up area of the study area has been increased from 7,017 hectares to 36,220 hectares during the 1972 -2012 period. This expansion has been accompanied by the loss of agricultural and forest land. There has been a decrease of approximately 10,000 hectares in cropped area and 2,000 hectares in forest land of the study area during the 1998-2012 inter-censal period. Corroboration of official census data, remote sensing results and field based qualitative data supports the view that high population growth rate, industrialization, better educational and transportation facilities and proximity of the study area to the capital (Islamabad) are the major factors of urban expansion and resulting land cover changes The present research is expected to have significant implications for other rapidly urbanizing areas of Pakistan in particular, and the Global South in general, in delivering baseline information about long term land use/cover changes

    Global Forest Monitoring from Earth Observation

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    Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies
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