Distinction of Deforestation Drivers in the Amazon Using Synthetic Aperture Radar (SAR) C-Band Data

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Особенности спектрального отклика залежных земель в различных природно-климатических условиях европейской территории России

В статье изложены результаты количественной оценки спектрально-отражательных признаков залежных земель, расположенных в различных природно-климатических условиях европейской территории России: лесной зоне, лесостепной и степно

Rapid visual presentation to support geospatial big data processing

Given the limited number of human GIS/image analysts at any organization, use of their time and organizational resources is important, especially in light of Big Data application scenarios when organizations may be overwhelmed with vast amounts of geospatial data. The current manuscript is devoted to the description of experimental research outlining the concept of Human-Computer Symbiosis where computers perform tasks, such as classification on a large image dataset, and, in sequence, humans perform analysis with Brain-Computer Interfaces (BCIs) to classify those images that machine learning had difficulty with. The addition of the BCI analysis is to utilize the brain\u27s ability to better answer questions like: Is the object in this image the object being sought? In order to determine feasibility of such a system, a supervised multi-layer convolutional neural network (CNN) was trained to detect the difference between ships\u27 and no ships\u27 from satellite imagery data. A prediction layer was then added to the trained model to output the probability that a given image was within each of those two classifications. If the probabilities were within one standard deviation of the mean of a gaussian distribution centered at 0.5, they would be stored in a separate dataset for Rapid Serial Visual Presentations (RSVP), implemented with PsyhoPy, to a human analyst using a low cost EMOTIV Insight EEG BCI headset. During the RSVP phase, hundreds of images per minute can be sequentially demonstrated. At such a pace, human analysts are not capable of making any conscious decisions about what is in each image; however, the subliminal aha-moment still can be detected by the headset. The discovery of these moments are parsed out by exposition of Event Related Potentials (ERPs), specifically the P300 ERPs. If a P300 ERP is generated for detection of a ship, then the relevant image would be moved to its rightful designation dataset; otherwise, if the image classification is still unclear, it is set aside for another RSVP iteration where the time afforded to the analyst for observation of each image is increased each time. If classification is still uncertain after a respectable amount of RSVP iterations, the images in question would be located within the grid matrix of its larger image scene. The adjacent images to those of interest on the grid would then be added to the presentation to give an analyst more contextual information via the expanded field of view. If classification is still uncertain, one final expansion of the field of view is afforded. Lastly, if somehow the classification of the image is indeterminable, the image is stored in an archive dataset

Resource Communication : ForestAz - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal)

AIM OF STUDY: ForestAz application was developed to (i) map Azorean forest areas accurately through semiautomatic supervised classification; (ii) assess vegetation condition (e.g., greenness and moisture) by computing and comparing several spectral indices; and (iii) quantitatively evaluate the stocks and dynamics of aboveground carbon (AGC) sequestrated by Azorean forest areas. AREA OF STUDY: ForestAz focuses primarily on the Public Forest Perimeter of S. Miguel Island (Archipelago of the Azores, Portugal), with about 3808 hectares. MATERIAL AND METHODS: ForestAz was developed with Javascript for the Google Earth Engine platform, relying solely on open satellite remote sensing data, as Copernicus Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (multispectral). MAIN RESULTS: By accurately mapping S. Miguel island forest areas using a detailed species-based vegetation mapping approach; by allowing frequent and periodic monitoring of vegetation condition; and by quantitatively assessing the stocks and dynamics of AGC by these forest areas, this remote sensing-based application may constitute a robust and low-cost operational tool able to support local/regional decision-making on forest planning and management. RESEARCH HIGHLIGHTS: This collaborative initiative between the University of the Azores and the Azores Regional Authority in Forest Affairs was selected to be one of the 99 user stories by local and regional authorities described in the catalog edited by the European Commission, the Network of European Regions Using Space Technologies (NEREUS Association), and the European Space Agency (ESA).info:eu-repo/semantics/publishedVersio

Effects of Rural Land Tenure System on Mangroves Management in Corentyne, Guyana

Mangrove forests in Guyana are recognized as the most important soft-engineering structure that protects the low-lying coastal areas against wave and wind actions. However, this vegetation has become severely degraded along some sections of the coast as a result of excessive exploitation and the dynamic nature of the coastline. In an attempt to protect and manage the mangrove ecosystem, the Government of Guyana has instituted a number of mechanisms, including the Guyana Mangrove Restoration Project (GMRP). However, the effectiveness of these instruments has been impaired by the different types of land tenure systems. The study aimed at exploring the inter-relationships between land use and tenure issues, and the sustainable management of mangroves in selected villages in Corentyne, Guyana with a view in determining plausible remedies. The study used a mixed-methods approach, involving Google Earth technology, observation, in-depth interviews, and questionnaire surveys. The results showed that while land use has not changed significantly over the past decade, the advancement and proliferation of mangroves on privately owned lands were quite noticeable. This has given rise to a new area of conflict between managers of coastal mangrove forests and land owners and small-scale traditional users, signifying an urgent need for policy reform

Using Unmanned Aircraft Systems to Identify Invasive Species

Invasive species serve as a threat to native biodiversity and ecosystem sustainability. Combatting the spread of invasive species requires long-term physical and monetary commitments. In Balule Nature Reserve of Greater Kruger National Park, South Africa, Opuntia ficus-inidica (the common prickly pear) has been a relentless invader, displacing the local flora and fauna. The goal of this project is to battle invasive species such as prickly pear using efficient and inexpensive technology: unmanned aerial vehicles (UAVs or drones) and multispectral sensors. Using a 4-bandwidth Parrot Sequoia multispectral sensor in tandem with the DJI Phantom Pro 3TM UAV, images of land plots were collected in the summer of 2018 on Balule Nature Reserve and surrounding areas in South Africa. From the images collected, maps were created using the mapping software Pix4D Mapper. Vegetation indices were created in which certain properties of vegetation are highlighted, assisting in plant identification. Using geographical informational system (GIS) software, classifications will be performed in which the multispectral data serves an important role. Multispectral sensors capture images in varying bandwidths; by collecting images in the red, green, red edge, and near-infrared bandwidths, there is potential for creating unique spectral signatures specific to individual objects such as prickly pear. Once a spectral signature is determined, a computer can then potentially perform unsupervised classifications to identify prickly pear solely from aerial images

Monitoring vegetation using remote sensing time series data: a review of the period 1996-2017

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources.Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth’s lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising Scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%