Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Review and critical analysis on digital elevation models

Nowadays, digital elevation model (DEM) acts as an inevitable component in the field of remote sensing and GIS. DEM reflects the physical surface of the earth helps to understand the nature of terrain by means of interpreting the landscape using modern techniques and high-resolution satellite images. To understand and analyze the nature of the terrain, DEM is required in many fields in the improvement of developing the product and decision making, mapping purpose, preparing 3D simulations, estimating river channel and creating contour maps to extract the elevation and so on. DEM in various applications will be useful to replicate the overall importance of the availability of worldwide, consistent, high-quality digital elevation models. The present article represents the overall review of DEMs, its generation, development using various techniques derived from topographic maps and high-resolution satellite images over a decade to present. It is useful to understand the nature of topography, address the practical problems and fix them by applying innovative ideas, upcoming high-resolution satellite images and techniques.Danas, digitalni model uzdizanja (DEM) djeluje kao neizbježna komponenta u području daljinskog istraživanja i GIS-a. DEM reflektira fizičku površinu zemlje pomaže pri razumijevanju prirode terena pomoću tumačenja krajolika pomoću suvremenih tehnika i satelitskih slika visoke razlučivosti. Za razumijevanje i analizu prirode terena, DEM je potreban u mnogim područjima poboljšanja razvoja proizvoda i odlučivanja, svrhe mapiranja, pripreme 3D simulacija, procjene riječnog kanala i stvaranja konturnih karata za izdvajanje visine i tako dalje. DEM u raznim aplikacijama bit će korisno za repliciranje sveukupne važnosti dostupnosti svjetskih, dosljednih i visokokvalitetnih modela digitalnih elevacija. Ovaj članak predstavlja cjelokupni pregled DEM-ova, njegovog stvaranja, razvoja pomoću različitih tehnika izvedenih iz topografskih karata i satelitskih snimaka visoke razlučivosti tijekom desetljeća do danas. Korisno je razumjeti prirodu topografije, rješavati praktične probleme i popraviti ih primjenom inovativnih ideja, nadolazećih satelitskih slika i tehnika visoke razlučivosti

Investigating the Aerodynamic Surface Roughness Length over Baghdad City Utilizing Remote Sensing and GIS Techniques

This study calculated the surface roughness length (Zo), zero-displacement length (Zd) and height of the roughness elements (ZH) using GIS applications. The practical benefit of this study is to classify the development of Baghdad, choose the appropriate places for installing wind turbines, improve urban planning, find rates of turbulence, pollution and others. The surface roughness length (Zo) of Baghdad city was estimated based on the data of the wind speed obtained from an automatic weather station installed at Al-Mustansiriyah University, the data of the satellite images digital elevation model (DEM), and the digital surface model (DSM), utilizing Remote Sensing Techniques. The study area was divided into 15 municipalities (Rasheed, Mansour, Shulaa, Karrada, Shaab, Adhamiyah, Sadre 2, Sadre 1, Rusafa, Alghadeer, Baghdad Aljadeedah, Karkh, Kadhumiya, Green zone, and Dora). The results indicated that the variations in Zo depend strongly on zero-displacement length (Zd) and the roughness element height (ZH) and wind speed. The research results demonstrated that Baghdad Aljadeedah has the largest (Zo) with 0.43 m and Rasheed has the lowest value of (Zo) with 0.19 m.; the average (Zo) of Baghdad city was 0.32 m

Algoritma penurasan data lidar untuk penjanaan model ketinggian digital bagi kawasan tropika

Filtering technique and the environmental factors are among the main factors, which affect Digital Elevation Model (DEM) accuracy obtained from the Light Detection and Ranging (LiDAR) data especially for steep area and covered by vegetation. Intensive research of LiDAR data filtering in tropical area is very limited and the improvement of the filtering technique using the environmental factor is very much needed. The purpose of this research is to improve the existing filtering techniques such as Progressive Morphology (PM) for DEM generation in the area covered by tropical vegetation. Initial test has been done by evaluating the filtering techniques such as PM, Adaptive Triangular Irregular Network (ATIN) and Elevation Threshold with Expand Window (ETEW) on the LiDAR data over Pekan, Pahang with slope between 0o and l0o. LiDAR DEM accuracy that was calculated based on ground reference point in mixed forest area shows that PM and ETEW filtering methods have produced minor RSME errors of A.226m and 0.192m compared to ATIN with 0.235m. Subsequent test was conducted for rubber area with slope value between 0o to 15o. The results show low RMSE error of 0.660m, 0.699m and A.717m for PM, ETEW and ATIN respectively. This shows that the slope parameter has an impact on the accuracy of the DEM, These results also demonstrate that the PM technique provides the highest accuracy. However the slope value in PM technique was based on constant value and applied to the entire LiDAR data. Compared to other filtering techniques, PM techniques provide more convenient way of improving the slope value. Improvement of PM filtering technique has been made by taking into account the actual slope value parameter and the revised method named AdapMorf algorithm. AdapMorf filtering technique was evaluated based on the slope gradient of the earth surface with the accuracy of the DEM error was evaluated for each area (i.e. mixed forest, rubber and oil palm) with slope between 0o and l5o. Three categories of assessments were carried out for each landcover and each category has a series of tests. DEM results were analyzed using RMSE error and the calculation of Type I and Type II errors. The best DEM's accuracy for AdapMorf by the types of landcover are 0.650m, 0.520m and 0.604m for mixed forests, rubber and oil palm respectively. The lowest results for Type I error are29.l7o/o,31.760/o and 35.l3Yo for rubber, mixed forest and oil palm respectively. The results for Type II error are 0.05oh,0.06% and 0.2lYs for rubber, mix forest and oil palm respectively. Due to the Type I error for AdapMorf was relatively high, the filtering technique was improved by introducing TyMof filtering technique. The tests were canied out and the results obtained show improvement in DEM's accuracy with RMSE for rubber and mixed forest are A.472m and 0.582m respectively. The Type I error for mixed forest and rubber are 28.90Yo and 19.29o/o respectively. This study shows that AdapMorf and TyMof filtering techniques were able to generate DEM with error smaller than the previous techniques for area with slope between 0o and 15". As a conclusion, AdapMorf and TyMof filtering techniques have shown that it can produce better quality of DEM for steep area and vegetated cover of tropical forest

Algoritma penurasan data lidar untuk penjanaan model ketinggian digital bagi kawasan tropika

Filtering technique and the environmental factors are among the main factors, which affect Digital Elevation Model (DEM) accuracy obtained from the Light Detection and Ranging (LiDAR) data especially for steep area and covered by vegetation. Intensive research of LiDAR data filtering in tropical area is very limited and the improvement of the filtering technique using the environmental factor is very much needed. The purpose of this research is to improve the existing filtering techniques such as Progressive Morphology (PM) for DEM generation in the area covered by tropical vegetation. Initial test has been done by evaluating the filtering techniques such as PM, Adaptive Triangular Irregular Network (ATIN) and Elevation Threshold with Expand Window (ETEW) on the LiDAR data over Pekan, Pahang with slope between 0o and l0o. LiDAR DEM accuracy that was calculated based on ground reference point in mixed forest area shows that PM and ETEW filtering methods have produced minor RSME errors of A.226m and 0.192m compared to ATIN with 0.235m. Subsequent test was conducted for rubber area with slope value between 0o to 15o. The results show low RMSE error of 0.660m, 0.699m and A.717m for PM, ETEW and ATIN respectively. This shows that the slope parameter has an impact on the accuracy of the DEM, These results also demonstrate that the PM technique provides the highest accuracy. However the slope value in PM technique was based on constant value and applied to the entire LiDAR data. Compared to other filtering techniques, PM techniques provide more convenient way of improving the slope value. Improvement of PM filtering technique has been made by taking into account the actual slope value parameter and the revised method named AdapMorf algorithm. AdapMorf filtering technique was evaluated based on the slope gradient of the earth surface with the accuracy of the DEM error was evaluated for each area (i.e. mixed forest, rubber and oil palm) with slope between 0o and l5o. Three categories of assessments were carried out for each landcover and each category has a series of tests. DEM results were analyzed using RMSE error and the calculation of Type I and Type II errors. The best DEM's accuracy for AdapMorf by the types of landcover are 0.650m, 0.520m and 0.604m for mixed forests, rubber and oil palm respectively. The lowest results for Type I error are29.l7o/o,31.760/o and 35.l3Yo for rubber, mixed forest and oil palm respectively. The results for Type II error are 0.05oh,0.06% and 0.2lYs for rubber, mix forest and oil palm respectively. Due to the Type I error for AdapMorf was relatively high, the filtering technique was improved by introducing TyMof filtering technique. The tests were canied out and the results obtained show improvement in DEM's accuracy with RMSE for rubber and mixed forest are A.472m and 0.582m respectively. The Type I error for mixed forest and rubber are 28.90Yo and 19.29o/o respectively. This study shows that AdapMorf and TyMof filtering techniques were able to generate DEM with error smaller than the previous techniques for area with slope between 0o and 15". As a conclusion, AdapMorf and TyMof filtering techniques have shown that it can produce better quality of DEM for steep area and vegetated cover of tropical forest

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

This open access book is a consolidation of lessons learnt and experiences gathered from our efforts to utilise Earth observation (EO) science and applications to address environmental challenges in the Hindu Kush Himalayan region. It includes a complete package of knowledge on service life cycles including multi-disciplinary topics and practically tested applications for the HKH. It comprises 19 chapters drawing from a decade’s worth of experience gleaned over the course of our implementation of SERVIR-HKH – a joint initiative of NASA, USAID, and ICIMOD – to build capacity on using EO and geospatial technology for effective decision making in the region. The book highlights SERVIR’s approaches to the design and delivery of information services – in agriculture and food security; land cover and land use change, and ecosystems; water resources and hydro-climatic disasters; and weather and climate services. It also touches upon multidisciplinary topics such as service planning; gender integration; user engagement; capacity building; communication; and monitoring, evaluation, and learning. We hope that this book will be a good reference document for professionals and practitioners working in remote sensing, geographic information systems, regional and spatial sciences, climate change, ecosystems, and environmental analysis. Furthermore, we are hopeful that policymakers, academics, and other informed audiences working in sustainable development and evaluation – beyond the wider SERVIR network and well as within it – will greatly benefit from what we share here on our applications, case studies, and documentation across cross-cutting topics

Land Use Planning for Natural Hazards

Natural hazard events are able to significantly affect the natural and artificial environment. In this context, changes in landforms due to natural disasters have the potential to affect and, in some cases, even restrict human interaction with the ecosystem. In order to minimize fatalities and reduce the economic impact that accompanies their occurrence, proper planning is crucial. Land use planning can play an important role in reducing current and future risks related to natural hazards. Land use changes can lead to natural hazards and vice versa: natural hazards affect land uses. Therefore, planners may take into account areas that are susceptible to natural hazards when selecting favorable locations for land use development. Appropriate land use planning can lead to the determination of safe and non-safe areas for urban activities. This Special Issue focuses on land use planning for natural hazards. In this context, various types of natural hazards, such as land degradation and desertification, coastal hazard, floods, and landslides, as well as their interactions with human activities, are presented

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing