Improving the quantification of land cover pressure on stream ecological status at the riparian scale using High Spatial Resolution Imagery

The aim of this paper is to demonstrate the interest of High Spatial Resolution Imagery (HSRI) and the limits of coarse land cover data such as CORINE Land Cover (CLC), for the accurate characterization of land cover structure along river corridors and of its functional links with freshwater ecological status on a large scale. For this purpose, we compared several spatial indicators built from two land cover maps of the Herault river corridor (southern France): one derived from the CLC database, the other derived from HSRI. The HSRI-derived map was obtained using a supervised object-based classification of multi-source remotely-sensed images (SPOT 5 XS-10 m and aerial photography-0.5 m) and presents an overall accuracy of 70 %. The comparison between the two sets of spatial indicators highlights that the HSRI-derived map allows more accuracy in the quantification of land cover pressures near the stream: the spatial structure of the river landscape is finely resolved and the main attributes of riparian vegetation can be quantified in a reliable way. The next challenge will consist in developing an operational methodology using HSRI for large-scale mapping of river corridor land cover,, for spatial indicator computation and for the development of related pressure/impact models, in order to improve the prediction of stream ecological status

Quantitative Spatial Upscaling of Categorical Data in the Context of Landscape Ecology: A New Scaling Algorithm

Spatially explicit ecological models rely on spatially exhaustive data layers that have scales appropriate to the ecological processes of interest. Such data layers are often categorical raster maps derived from high-resolution, remotely sensed data that must be scaled to a lower spatial resolution to make them compatible with the scale of ecological analysis. Statistical functions commonly used to aggregate categorical data are majority-, nearest-neighbor- and random-rule. For heterogeneous landscapes and large scaling factors, however, use of these functions results in two critical issues: (1) ignoring large portions of information present in the high-resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (2) maintaining classes from the high-resolution dataset at the lower spatial resolution assumes validity of the classification scheme at the low-resolution scale, failing to represent recurring mixes of heterogeneous classes present in the low-resolution grid cells. The proposed new scaling algorithm resolves these issues, aggregating categorical data while simultaneously controlling for information loss by generating a non-hierarchical, representative, classification system valid at the aggregated scale. Implementing scaling parameters, that control class-label precision effectively reduced information loss of scaled landscapes as class-label precision increased. In a neutral-landscape simulation study, the algorithm consistently preserved information at a significantly higher level than the other commonly used algorithms. When applied to maps of real landscapes, the same increase in information retention was observed, and the scaled classes were detectable from lower-resolution, remotely sensed, multi-spectral reflectance data with high accuracy. The framework developed in this research facilitates scaling-parameter selection to address trade-offs among information retention, label fidelity, and spectral detectability of scaled classes. When generating high spatial resolution land-cover maps, quantifying effects of sampling intensity, feature-space dimensionality and classifier method on overall accuracy, confidence estimates, and classifier efficiency allowed optimization of the mapping method. Increase in sampling intensity boosted accuracies in a reasonably predictable fashion. However, adding a second image acquired when ground conditions and vegetation phenology differed from those of the first image had a much greater impact, increasing classification accuracy even at low sampling intensities, to levels not reached with a single season image

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion

Deep Learning Monitoring of Woody Vegetation Density in a South African Savannah Region

Bush encroachment in African savannahs has been identified as a land degradation process, mainly due to the detrimental effect it has on small pastoralist communities. Mapping and monitoring the extent covered by the woody component in savannahs has therefore become the focus of recent remote sensing-based studies. This is mainly due to the large spatial scale that the process of woody vegetation encroachment is related with and the fact that appropriate remote sensing data are now available free of charge. However, due to the nature of savannahs and the mixture of land cover types that commonly make up the signal of a single pixel, simply mapping the presence/absence of woody vegetation is somewhat limiting: it is more important to know whether an area is undergoing an increase in woody cover, ever if it is not the dominant cover type. More recent efforts have, therefore, focused in mapping the fraction of woody vegetation, which, clearly, is much more challenging. This paper proposes a methodological framework for mapping savannah woody vegetation and monitoring its evolution though time, based on very high-resolution data and multi-temporal medium-scale satellite imagery. We tested our approach in a South African savannah region, the Northwest Province (>100,000 km2), 0.5m-pixel aerial photographs for sampling and validation and Landsat data

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

What's exposed? Mapping elements at risk from space

The world has suffered from severe natural disasters over the last decennium. The earthquake in Haiti in 2010 or the typhoon “Haiyan” hitting the Philippines in 2013 are among the most prominent examples in recent years. Especially in developing countries, knowledge on amount, location or type of the exposed elements or people is often not given. (Geo)-data are mostly inaccurate, generalized, not up-to-date or even not available at all. Thus, fast and effective disaster management is often delayed until necessary geo-data allow an assessment of effected people, buildings, infrastructure and their respective locations. In the last decade, Earth observation data and methods have developed a product portfolio from low resolution land cover datasets to high resolution spatially accurate building inventories to classify elements at risk or even assess indirectly population densities. This presentation will give an overview on the current available products and EO-based capabilities from global to local scale. On global to regional scale, remote sensing derived geo-products help to approximate the inventory of elements at risk in their spatial extent and abundance by mapping and modelling approaches of land cover or related spatial attributes such as night-time illumination or fractions of impervious surfaces. The capabilities and limitations for mapping physical exposure will be discussed in detail using the example of DLR’s ‘Global Urban Footprint’ initiative. On local scale, the potential of remote sensing particularly lies in the generation of spatially and thematically accurate building inventories for the detailed analysis of the building stock’s physical exposure. Even vulnerability-related indicators can be derived. Indicators such as building footprint, height, shape characteristics, roof materials, location, and construction age and structure type have already been combined with civil engineering approaches to assess building stability for large areas. Especially last generation optical sensors – often in combination with digital surface models – featuring very high geometric resolutions are perceived as advantageous for operational applications, especially for small to medium scale urban areas. With regard to user-oriented product generation in the FP-7project SENSUM, a multi-scale and multi-source reference database has been set up to systematically screen available products – global to local ones – with regard to data availability in data-rich and data-poor countries. Thus, the higher ranking goal in this presentation is to provide a systematic overview on EO-based data sets and their individual capabilities and limitations with respect to spatial, temporal and thematic details to support decision-making in before, during and after natural disasters

Analisis Fusi Data Multi Sensor Menggunakan Algoritma Spatial and Temporal Adaptive Reflectance Fusion Model (Studi Kasus : WorldView-3 dan Landsat 8)

Citra satelit resolusi tinggi cocok digunakan untuk pemetaan tutupan lahan skala besar. Namun karena keterbatasan teknik dan biaya, ketersediaan data multi temporal dapat dikatakan terbatas. Untuk mengatasi masalah tersebut maka dilakukan pengolahan citra, yaitu teknik fusi. Metode fusi yang umum digunakan seperti intensity-hue-saturation (IHS) transformation, principle component substitution (PCS), dan Brovey transformation difokuskan untuk menghasilkan citra dengan spasial dan spektral tinggi pengkombinasian data pankromatik dengan data multispektral yang diperoleh dari citra tersebut secara serempak. Pada penelitian ini, algoritma Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) dikembangkan untuk mendapatkan data citra dengan resolusi spasial tinggi dan cakupan area secara berkala. Kanal multispektral data multi sensor dari WorldView-3 dan Landsat 8 dimanfaatkan untuk memperoleh hasil fusi yang disebut sebagai citra sintetik. Untuk menguji performa STARFM, uji akurasi dilakukan dengan membuat training sample pada hasil citra sintetik. Akurasi dilakukan dengan melihat keterpisahan antar kelas menggunakan metode matriks konfusi untuk menganalisa akurasi tutupan lahan yang teridentifikasi. Fusi dengan metode ini menunjukkan peningkatan kualitas citra sintetik, diindikasi dengan lebih banyaknya jumlah objek yang dapat diidentifikasi pada citra sintetik (Kappa = 0,68 dan 0,64) yang dikombinasikan dengan citra Landsat asli. Dengan resolusi spasial tinggi dan informasi temporal berkala, maka proses klasifikasi dan interpretasi objek menjadi lebih terbantu guna kebutuhan analisis pemetaan dan pemantauan tutupan lahan. ===================================================================================================== A high resolution satellite image was suitable for large-scale land cover mapping. However due to technical limitations and budget, the availability of multi-temporal data was limited. Therefore image processing i.e. fusion technique was performed for solving this problem. Common methods such as intensity-hue-saturation (IHS) transformation, principle component substitution (PCS), and Brovey transformation are more focused on generating images which combine high-spatial resolution panchromatic data with multispectral data obtained from the image simultaneously. In this research, multispectral channels from WorldView-3 and Landsat 8 were utilized to obtain fusion result named synthetic images. To assess the performance of STARFM fusion method, an accuracy test was performed by creating sample training based on synthetic images. The accuracy was tested by looking at the separation between classes by the confusion matrix method to analyze the accuracy identified land cover. This fusion was improved the quality of synthetic images that indicated by more identifiable object features on synthetic image (Kappa=0.68 and 0.64) compared to the original Landsat image. With finer spatial feature and more frequent temporal information, the classification and interpretation of object would be useful for land cover mapping and monitoring

A deep-learning approach for multi-temporal savannah woody vegetation density assessment with Earth Observation data

Bush encroachment in African savannahs has been identified as a land degradation process, mainly due to the detrimental effect it has on small pastoralist communities. Mapping and monitoring the extent covered by the woody component in savannahs has therefore become the focus of recent remote sensing-based studies. This is mainly due to the large spatial scale that the process of woody vegetation encroachment is related with and the fact that appropriate remote sensing data are now available free of charge. However, due to the nature of savannahs and the mixture of land cover types that commonly make up the signal of a single pixel, simply mapping the presence/absence of woody vegetation is somewhat limiting: it is more important to know whether an area is undergoing an increase in woody cover, ever if it is not the dominant cover type. More recent efforts have, therefore, focused in mapping the fraction of woody vegetation, which, clearly, is much more challenging. This paper proposes a methodological framework for mapping savannah woody vegetation and monitoring its evolution though time, based on very high-resolution data and multi-temporal medium-scale satellite imagery. We tested our approach in a South African savannah region, the Northwest Province (>100,000 km2), 0.5m-pixel aerial photographs for sampling and validation and Landsat data. We first mapped presence/absence of woody vegetation using samples selected over 5x5 km aerial photo subsets acquired between 2009 and 2013 and a Random Forest classifier. We then used these estimates to train a U-Net Convolutional Neural Network to produce fractional woody cover estimates from a series of spatio-temporal variability metrics derived from all available Landsat data in the five years between 2009 and 2013. The model was then applied to other epochs of Landsat metrics, centred around 2016, 2006, 2001, 1998, 1993, and 1988. The multi-temporal fractional woody cover maps were also used to derive estimates of fractional woody cover change over the three decades of the study period. We identified areas that had undergone a constant increase in woody cover density through the 6 epochs, and others that saw a net increase in woody cover density from 1988 to 2018. These hotspots of woody cover densification, or encroachment, that our methodology was able to identify, should be the ones that mitigation measures are directed to, in order to prioritise action and limit the extent and damage caused by this form of savannah land degradation