Pansharpening techniques to detect mass monument damaging in Iraq

The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology, for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in unsafe area, is reported. The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of damages using VHR images

Commercial forest species discrimination and mapping using cost effective multispectral remote sensing in midlands region of KwaZulu-Natal province, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg, 2018.Discriminating forest species is critical for generating accurate and reliable information necessary for sustainable management and monitoring of forests. Remote sensing has recently become a valuable source of information in commercial forest management. Specifically, high spatial resolution sensors have increasingly become popular in forests mapping and management. However, the utility of such sensors is costly and have limited spatial coverage, necessitating investigation of cost effective, timely and readily available new generation sensors characterized by larger swath width useful for regional mapping. Therefore, this study sought to discriminate and map commercial forest species (i.e. E. dunii, E.grandis, E.mix, A.mearnsii, P.taedea and P.tecunumanii, P.elliotte) using cost effective multispectral sensors. The first objective of this study was to evaluate the utility of freely available Landsat 8 Operational Land Imager (OLI) in mapping commercial forest species. Using Partial Least Square Discriminant Analysis algorithm, results showed that Landsat 8 OLI and pan-sharpened version of Landsat 8 OLI image achieved an overall classification accuracy of 79 and 77.8%, respectively, while WorldView-2 used as a benchmark image, obtained 86.5%. Despite low spatial of resolution 30 m, result show that Landsat 8 OLI was reliable in discriminating forest species with reasonable and acceptable accuracy. This freely available imagery provides cheaper and accessible alternative that covers larger swath-width, necessary for regional and local forests assessment and management. The second objective was to examine the effectiveness of Sentinel-1 and 2 for commercial forest species mapping. With the use of Linear Discriminant Analysis, results showed an overall accuracy of 84% when using Sentinel 2 raw image as a standalone data. However, when Sentinel 2 was fused with Sentinel’s 1 Synthetic Aperture Radar (SAR) data, the overall accuracy increased to 88% using Vertical transmit/Horizontal receive (VH) polarization and 87% with Vertical transmit/Vertical receive (VV) polarization datasets. The utility of SAR data demonstrates capability for complementing Sentinel-2 multispectral imagery in forest species mapping and management. Overall, newly generated and readily available sensors demonstrated capability to accurately provide reliable information critical for mapping and monitoring of commercial forest species at local and regional scales

Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

[EN] The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery of burned forest ecosystems. Here, we aimed to assess the potential of combining multispectral imagery at different spectral and spatial resolutions to estimate soil indicators of burn severity. The study was conducted in a burned area located at the northwest of the Iberian Peninsula (Spain). One month after fire, we measured soil burn severity in the field using an adapted protocol of the Composite Burn Index (CBI). Then, we performed soil sampling to analyze three soil properties potentially indicatives of fire-induced changes: mean weight diameter (MWD), soil moisture content (SMC) and soil organic carbon (SOC). Additionally, we collected post-fire imagery from the Sentinel-2A MSI satellite sensor (10–20 m of spatial resolution), as well as from a Parrot Sequoia camera on board an unmanned aerial vehicle (UAV; 0.50 m of spatial resolution). A Gram-Schmidt (GS) image sharpening technique was used to increase the spatial resolution of Sentinel-2 bands and to fuse these data with UAV information. The performance of soil parameters as indicators of soil burn severity was determined trough a machine learning decision tree, and the relationship between soil indicators and reflectance values (UAV, Sentinel-2 and fused UAV-Sentinel-2 images) was analyzed by means of support vector machine (SVM) regression models. All the considered soil parameters decreased their value with burn severity, but soil moisture content, and, to a lesser extent, soil organic carbon discriminated at best among soil burn severity classes (accuracy = 91.18 %; Kappa = 0.82). The performance of reflectance values derived from the fused UAV-Sentinel-2 image to monitor the effects of wildfire on soil characteristics was outstanding, particularly for the case of soil organic carbon content (R2 = 0.52; RPD = 1.47). This study highlights the advantages of combining satellite and UAV images to produce spatially and spectrally enhanced images, which may be relevant for estimating main impacts on soil properties in burned forest areas where emergency actions need to be applied.S

Guidelines for Best Practice and Quality Checking of Ortho Imagery

For almost 10 years JRC's ¿Guidelines for Best Practice and Quality Control of Ortho Imagery¿ has served as a reference document for the production of orthoimagery not only for the purposes of CAP but also for many medium-to-large scale photogrammetric applications. The aim is to provide the European Commission and the remote sensing user community with a general framework of the best approaches for quality checking of orthorectified remotely sensed imagery, and the expected best practice, required to achieve good results. Since the last major revision (2003) the document was regularly updated in order to include state-of-the-art technologies. The major revision of the document was initiated last year in order to consolidate the information that was introduced to the document in the last five years. Following the internal discussion and the outcomes of the meeting with an expert panel it was decided to adopt as possible a process-based structure instead of a more sensor-based used before and also to keep the document as much generic as possible by focusing on the core aspects of the photogrammetric process. Additionally to any structural changes in the document new information was introduced mainly concerned with image resolution and radiometry, digital airborne sensors, data fusion, mosaicking and data compression. The Guidelines of best practice is used as the base for our work on the definition of technical specifications for the orthoimagery. The scope is to establish a core set of measures to ensure sufficient image quality for the purposes of CAP and particularly for the Land Parcel Identification System (PLIS), and also to define the set of metadata necessary for data documentation and overall job tracking.JRC.G.3-Agricultur

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

In South Africa, soil erosion is considered as an environmental and social problem with serious financial implications particularly in some rural areas where this geomorphological phenomenon is widespread. An example is the Umzimvubu Local Municipality, where most households are strongly reliant on agriculture for their livelihood. Sustainable agriculture and proper land management in these rural areas require information relevant to the spatial distribution of soil erosion. This study was therefore aimed at generating such information using Landsat8 Operational Land Imager (OLI)-derived vegetation indices (VIs) including the Normalised Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), as well as Soil and Atmospherically Resistance Vegetation Index (SARVI). Raster calculator in ArcMap10.2 was used to classify soil erosion features based on selected suitable thresholds in each VI. SPOT6/7 (Satellites Pour l’Obsevation de la Terre) multispectral data and Google Earth images were used for ground truth purposes. SAVI achieved the highest overall classification accuracy of 83% and kappa statistics of 64%, followed by NDVI and SARVI with equal overall accuracy of 81% and slightly different kappa statistics of 60% for the former and 59% for the latter. Using these indices, the study successfully mapped the spatial distribution of soil erosion within the study area albeit there were some challenges due to coarser spatial resolution (15mx15m) of Landsat8 image. Due to this setback, image fusion and pan-sharpening of Landsat8 with higher spatial resolution images is strongly suggested as an alternative to improve the Landsat8 spatial resolution.Keywords: Geographic Information System; Remote Sensing; Soil Erosion; Vegetation Indice

Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing

A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation

In this paper, we present several study cases focused on marine, oceanographic, and atmospheric environments, which would greatly benefit from the use of a deployable system for small satellite observations. As opposed to the large standard ones, small satellites have become an effective and affordable alternative access to space, owing to their lower costs, innovative design and technology, and higher revisiting times, when launched in a constellation configuration. One of the biggest challenges is created by the small satellite instrumentation working in the visible (VIS), infrared (IR), and microwave (MW) spectral ranges, for which the resolution of the acquired data depends on the physical dimension of the telescope and the antenna collecting the signal. In this respect, a deployable payload, fitting the limited size and mass imposed by the small satellite architecture, once unfolded in space, can reach performances similar to those of larger satellites. In this study, we show how ecology and Earth Observations can benefit from data acquired by small satellites, and how they can be further improved thanks to deployable payloads. We focus on DORA—Deployable Optics for Remote sensing Applications—in the VIS to TIR spectral range, and on a planned application in the MW spectral range, and we carry out a radiometric analysis to verify its performances for Earth Observation studies