Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status

Habitat quality assessments often demand wall‐to‐wall information about the state of vegetation. Remote sensing can provide this information by capturing optical and structural attributes of plant communities. Although active and passive remote sensing approaches are considered as complementary techniques, they have been rarely combined for conservation mapping. Here, we combined spaceborne multispectral Sentinel‐2 and Sentinel‐1 SAR data for a remote sensing‐based habitat quality assessment of dwarf shrub heathland, which was inspired by nature conservation field guidelines. Therefore, three earlier proposed quality layers representing (1) the coverage of the key dwarf shrub species, (2) stand structural diversity and (3) an index reflecting co‐occurring vegetation were mapped via linking in situ data and remote sensing imagery. These layers were combined in an RGB‐representation depicting varying stand attributes, which afterwards allowed for a rule‐based derivation of pixel‐wise habitat quality classes. The links between field observations and remote sensing data reached correlations between 0.70 and 0.94 for modeling the single quality layers. The spatial patterns shown in the quality layers and the map of discrete quality classes were in line with the field observations. The remote sensing‐based mapping of heathland conservation status showed an overall agreement of 76% with field data. Transferring the approach in time (applying a second set of Sentinel‐1 and ‐2 data) caused a decrease in accuracy to 73%. Our findings suggest that Sentinel‐1 SAR contains information about vegetation structure that is complimentary to optical data and therefore relevant for nature conservation. While we think that rule‐based approaches for quality assessments offer the possibility for gaining acceptance in both communities applied conservation and remote sensing, there is still need for developing more robust and transferable methods

Remote Sensing for Non‐Technical Survey

This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multi‐spectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyper‐spectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed

Change detection in multi-temporal SAR images

Change detection in multi-temporal SAR imagery it’s a useful tool for preventing natural disasters, studies of the terrain and many other utilities but it’s a tool in constant improvement always looking for better results and less computational costs. Though there are many ways to deal with it, the present thesis focuses in change detection using a generalization of the Kittler & Illingworth threshold (GKIT) algorithm which aim is to obtain an unsupervised method for change detection in multi-temporal images. However, the kittler & Illingworth method on his own it’s not enough in most of the cases for precise unsupervised change detection. Many factors affect the results. The most significant it’s the “speckle” noise, a common multiplicative noise in SAR images. K&I uses a ratio approach to discriminate the “speckle” noise and make it easier to eliminate. As the K&I it’s not enough to eliminate the noise many filters are implemented based on wavelet filtering. With two wavelet filtering approaches, based on the discrete wavelet transform (DWT) and on the stationary wavelet transform (SWT), the results are better but still imprecise. The solution to this drawback it’s a multi-channel fusion method based on the Markov random fields (MRF). The MRF method fuses filtered ratio images and the original one in order to obtain a final ratio image that eliminates most of the noise retaining most of the details. The use of all this concepts will make a very efficient and precise change detection in multi-temporal SAR images.Ingeniería Técnica de Telecomunicación, especialidad Sonido e ImagenTelekomunikazio Ingeniaritza Teknikoa. Soinua eta Irudia Berezitasun

Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data

Secondary forests play an important role in restoring carbon and biodiversity lost previously through deforestation and degradation and yet there is little information available on the extent of different successional stages. Such knowledge is particularly needed in tropical regions where past and current disturbance rates have been high but regeneration is rapid. Focusing on three areas in the Brazilian Amazon (Manaus, Santarém, Machadinho d'Oeste), this study aimed to evaluate the use of single-date Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data in the 2007–2010 period for i) discriminating mature forest, non-forest and secondary forest, and ii) retrieving the age of secondary forests (ASF), with 100 m × 100 m training areas obtained by the analysis of an extensive time-series of Landsat sensor data over the three sites. A machine learning algorithm (random forests) was used in combination with ALOS PALSAR backscatter intensity at HH and HV polarizations and Landsat 5 TM surface reflectance in the visible, near-infrared and shortwave infrared spectral regions. Overall accuracy when discriminating mature forest, non-forest and secondary forest is high (95–96%), with the highest errors in the secondary forest class (omission and commission errors in the range 4–6% and 12–20% respectively) because of misclassification as mature forest. Root mean square error (RMSE) and bias when retrieving ASF ranged between 4.3–4.7 years (relative RMSE = 25.5–32.0%) and 0.04–0.08 years respectively. On average, unbiased ASF estimates can be obtained using the method proposed here (Wilcoxon test, p-value > 0.05). However, the bias decomposition by 5-year interval ASF classes showed that most age estimates are biased, with consistent overestimation in secondary forests up to 10–15 years of age and underestimation in secondary forests of at least 20 years of age. Comparison with the classification results obtained from the analysis of extensive time-series of Landsat sensor data showed a good agreement, with Pearson's coefficient of correlation (R) of the proportion of mature forest, non-forest and secondary forest at 1-km grid cells ranging between 0.97–0.98, 0.96–0.98 and 0.84–0.90 in the 2007–2010 period, respectively. The agreement was lower (R = 0.82–0.85) when using the same dataset to compare the ability of ALOS PALSAR and Landsat 5 TM data to retrieve ASF. This was also dependent on the study area, especially when considering mapping secondary forest and retrieving ASF, with Manaus displaying better agreement when compared to the results at Santarém and Machadinho d'Oeste

Remote sensing and GIS in support of sustainable agricultural development

Over the coming decades it is expected that the vast amounts of area currently in agricultural production will face growing pressure to intensify as world populations continue to grow, and the demand for a more Western-based diet increases. Coupled with the potential consequences of climate change, and the increasing costs involved with current energy-intensive agricultural production methods, meeting goals of environmental and socioeconomic sustainability will become ever more challenging. At a minimum, meeting such goals will require a greater understanding of rates of change, both over time and space, to properly assess how present demand may affect the needs of future generations. As agriculture represents a fundamental component of modern society, and the most ubiquitous form of human induced landscape change on the planet, it follows that mapping and tracking changes in such environments represents a crucial first step towards meeting the goal of sustainability. In anticipation of the mounting need for consistent and timely information related to agricultural development, this thesis proposes several advances in the field of geomatics, with specific contributions in the areas of remote sensing and spatial analysis: First, the relative strengths of several supervised machine learning algorithms used to classify remotely sensed imagery were assessed using two image analysis approaches: pixel-based and object-based. Second, a feature selection process, based on a Random Forest classifier, was applied to a large data set to reduce the overall number of object-based predictor variables used by a classification model without sacrificing overall classification accuracy. Third, a hybrid object-based change detection method was introduced with the ability to handle disparate image sources, generate per-class change thresholds, and minimize map updating errors. Fourth, a spatial disaggregation procedure was performed on coarse scale agricultural census data to render an indicator of agricultural development in a spatially explicit manner across a 9,000 km2 watershed in southwest Saskatchewan for three time periods spanning several decades. The combination of methodologies introduced represents an overall analytical framework suitable for supporting the sustainable development of agricultural environments

Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

Novel pattern recognition methods for classification and detection in remote sensing and power generation applications

Novel pattern recognition methods for classification and detection in remote sensing and power generation application

Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future