Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Comparative model for classification of forest degradation

The challenges of forest degradation together with its related effects have attracted research from diverse disciplines, resulting in different definitions of the concept. However, according to a number of researchers, the central element of this issue is human intrusion that destroys the state of the environment. Therefore, the focus of this research is to develop a comparative model using a large amount of multi-spectral remote sensing data, such as IKONOS, QUICKBIRD, SPOT, WORLDVIEW-1, Terra-SARX, and fused data to detect forest degradation in Cameron Highlands. The output of this method in line with the performance measurement model. In order to identify the best data, fused data and technique to be employed. Eleven techniques have been used to develop a Comparative technique by applying them on fifteen sets of data. The output of the Comparative technique was used to feed the performance measurement model in order to enhance the accuracy of each classification technique. Moreover, a Performance Measurement Model has been used to verify the results of the Comparative technique; and, these outputs have been validated using the reflectance library. In addition, the conceptual hybrid model proposed in this research will give the opportunity for researchers to establish a fully automatic intelligent model for future work. The results of this research have demonstrated the Neural Network (NN) to be the best Intelligent Technique (IT) with a 0.912 of the Kappa coefficient and 96% of the overall accuracy, Mahalanobis had a 0.795 of the Kappa coefficient and 88% of the overall accuracy and the Maximum likelihood (ML) had a 0.598 of the Kappa coefficient and 72% of the overall accuracy from the best fused image used in this research, which was represented by fusing the IKONOS image with the QUICKBIRD image as finally employed in the Comparative technique for improving the detectability of forest change

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Remote sensing drought impacts on wetland vegetation productivity at the Soetendalsvlei in the Heuningnes Catchment, South Africa

>Magister Scientiae - MScThis work aimed at assessing the response of wetland vegetation productivity to the 2014-2017 climate-induced drought at the Soetendalsvlei wetland system in the Western Cape province of South Africa. To achieve this objective, firstly a literature review on the progress of remotely sensed data applications in assessing and monitoring wetland vegetation productivity was conducted. The review elaborates on the role of remote sensing in monitoring and assessing wetland vegetation productivity, with a detailed discussion of the climate change and variability impacts on wetland vegetation productivity. Accurate assessment results are produced when suitable processing techniques are selected as well as appropriate spatial and spectral resolution for extracting spectral information of wetland vegetation productivity. Secondly, wetland vegetation changes and productivity status was assessed using multi-temporal resolution Landsat series imagery and Normalized Difference Vegetation Index (NDVI) during the wet and dry seasons for the period between 2014 and 2018

Mapping the surface water storage variation in densely impounded semi-arid NE Brazil with satellite remote sensing approach

Surface water bodies provide vital support to the society and fundamentally affect ecosystems in various manners. Precise knowledge of the spatial extent of surface water bodies (e.g. reservoirs) as well as of the quantity of water they store is necessary for efficient water deployment and understanding of the local hydrology. Remote sensing provides broad opportunities for surface water mapping. The main objectives of this thesis are: 1) delineating surface water area of partly vegetated water bodies only from remote sensing data without field data input; 2) obtaining the surface water storage, and 3) analyzing its spatio-temporal variations for northeastern (NE) Brazil as a representative for a densely dammed semi-arid region. At first, I investigated the potential of digital elevation models (DEMs) generated from TanDEM-X data, which were acquired during the low water level stage, for reservoirs’ bathymetry derivation. I found that the accuracy of such DEMs can reach one meter, both in the absolute and relative respects. It has shown that DEMs derived from TanDEM-X data have great potentials for representing the reservoirs’ bathymetry of temporally dried-out reservoirs. Subsequently, I targeted at developing a method for mapping the water surface beneath canopy independent of field data for further delineation of the effective water surface. Instead of the commonly used backscattering coefficients, I investigated the capability of the Gray-Level Co-Occurrence Matrix (GLCM) texture index to distinguish different types of Radar backscattering taking place in (partly) vegetated reservoirs. This experiment demonstrated that different types of backscattering at the vegetated water surface show distinct statistical characteristics on GLCM variance derived from TerraSAR-X satellite time series data. Furthermore, with the threshold established based on the statistics of the sub-populations dominated by different types of backscattering, the vegetated water surfaces were effectively mapped, and the effective water surface areas were further delineated with an accuracy of 77% to 95%. ii Based on the investigation of the DEMs generated from TanDEM-X data, I derived the formerly unknown bathymetry for 2 105 reservoirs of various sizes in four representative regions of an overall area of 10 000 km2. The spatial distributions of surface water storage capacities in the four regions were subsequently extracted from the combination of the reservoir bathymetry and the water surface extents provided by RapidEye satellite time series. Furthermore, the spatio-temporal variations of surface water storage were derived for the four representative regions on an annual basis in the period of 2009-2017. This study showed that 1) The density of reservoirs in NE Brazil amounts to 0.04-0.23 reservoirs per km2, the corresponding water surface and surface water storage are 1.18-4.13 ha/km2 and 0.01-0.04 hm3 m/km², respectively; 2) On the spatial unit of 5×5 km2, the surface water storage in the region constantly decreased due to a prolonged drought with a rate of 105 m3/year from 2009 to 2017, with a slight increase from 2016 to 2017 in a few reservoirs; 3) Local precipitation deficit controls the variation of the overall surface water storage in the region. In this thesis I demonstrated the great potential of the great potential of SAR and optical satellite time series data for hydrological applications. The method I developed for delineating the effective water extent from the vegetated reservoirs has shown high potential transferability for other similar regions. The data gaps of bathymetry and surface waters storage capacity were filled for 2 105 reservoirs in NE Brazil. The results of the spatio-temporal variations of surface water storage in four representative regions from 2009-2016 can support future water management and improve hydrological prediction in NE Brazil

Estimativa de biomassa acima do solo de caatinga através de imagens SAR

A Caatinga é um bioma de ocorrência do semiárido do Brasil, tendo uma das maiores ocupações populacionais em terras secas no mundo. Porém, ainda há carência da aplicação de novas técnicas de estimativa de sua biomassa a partir de dados remotos. Sendo assim, o objetivo da tese foi avaliar a acurácia das imagens do Sentinel-1 na estimativa da biomassa acima do solo (BAS) da Caatinga no Alto Sertão do estado de Sergipe. A distribuição espacial e fenológica da Caatinga na região estudada foi analisada utilizando o Normalized Difference Vegetation Index (NDVI). A análise florística e fitossociológica foi realizada por meio do inventário florestal, utilizado também para calcular a BAS nos fragmentos de Caatinga. Foram testados diferentes métodos de filtragem para avaliar a eficácia na redução do speckle na imagem do Sentinel-1 analisando o número equivalente de looks (NEL). A estimativa da BAS utilizando imagens do Sentinel-1 utilizou dados do inventário em campo, comparando as acurácias das respostas de filtros a partir da decomposição polarimétrica e, posteriormente, testando os atributos: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropia (H), Ângulo Alpha (α), por meio de regressões lineares simples e múltiplas, na Caatinga Verde, Intermediária e Seca. A Caatinga estudada não é influenciada pelos fatores fisiográficos: declividade, altimetria, proximidade ao rio e tipo de solo. A Caatinga densa apresenta curvas fenológicas com maior condição de verdor que a aberta. A espécie Cenostigma pyramidale é a mais abundante entre as 25 identificadas. O filtro Gamma apresentou melhor desempenho na redução do speckle. A comparação da BAS estimada e observada indicou que a regressão múltipla fornece melhor acurácia nos períodos de Verdor (R2: 0,72) e Intermediário (R2: 0,73) da vegetação, com a contribuição de atributos coerentes e incoerentes. Portanto, o estudo permitiu analisar espacialmente a Caatinga estudada, caracterizando-a fenologicamente bem como sua composição e fitossociologia. Também foi possível verificar as diferentes atenuações do speckle no pré- processamento das imagens. Por fim, constatou-se que as imagens do Sentinel-1 podem ser utilizadas para a estimar a BAS.The Caatinga is a biome occurring in the semiarid region of Brazil, having one of the largest population occupations in dry lands in the world. However, there is still a lack of application of new techniques for estimating its biomass from remote data. Therefore, the objective of the thesis was to evaluate the accuracy of Sentinel-1 images in estimating the aboveground biomass (BAS) of the Caatinga in the Alto Sertão of the state of Sergipe. The spatial and phenological distribution of the Caatinga in the studied region was analyzed using the Normalized Difference Vegetation Index (NDVI). The floristic and phytosociological analysis was carried out through the forest inventory, also used to calculate the BAS in the Caatinga fragments. Different filtering methods were tested to evaluate the effectiveness of speckle reduction in the Sentinel-1 image by analyzing the equivalent number of looks (NEL). The BAS estimate using Sentinel-1 images used field inventory data comparing the accuracy of filter responses from the polarimetric decomposition and, later, testing the attributes: VV, VH, VH/VV, Radar Vegetation Index (RVI), Dual Polarization SAR Vegetation Index (DPSVI), Entropy (H), Alpha Angle (α), through simple and multiple linear regressions, in the Greenness, Intermediate and Dry Caatinga. The studied Caatinga is not influenced by physiographic factors: slope, altimetry, proximity to the river and type of soil. Dense Caatinga has phenological curves with greater greenness than open one. The Cenostigma pyramidale species is the most abundant among the 25 identified. The Gamma filter showed better performance in speckle reduction. The comparison of the estimated and observed BAS indicated that the multiple regression provides better accuracy in the Greenness (R2: 0.72) and Intermediate (R2: 0.73) periods of the vegetation, with the contribution of coherent and incoherent attributes. Therefore, the study allowed the spatial analysis of the studied Caatinga, characterizing it phenologically as well as its composition and phytosociology. It was also possible to verify the different attenuations of the speckle in the pre-processing of the images. Finally, it was found that Sentinel-1 images can be used to estimate BAS

Wetland mapping and monitoring using polarimetric and interferometric synthetic aperture radar (SAR) data and tools

Wetlands are home to a great variety of flora and fauna species and provide several unique environmental functions, such as controlling floods, improving water-quality, supporting wildlife habitat, and shoreline stabilization. Detailed information on spatial distribution of wetland classes is crucial for sustainable management and resource assessment. Furthermore, hydrological monitoring of wetlands is also important for maintaining and preserving the habitat of various plant and animal species. This thesis investigates the existing knowledge and technological challenges associated with wetland mapping and monitoring and evaluates the limitations of the methodologies that have been developed to date. The study also proposes new methods to improve the characterization of these productive ecosystems using advanced remote sensing (RS) tools and data. Specifically, a comprehensive literature review on wetland monitoring using Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques is provided. The application of the InSAR technique for wetland mapping provides the following advantages: (i) the high sensitivity of interferometric coherence to land cover changes is taken into account and (ii) the exploitation of interferometric coherence for wetland classification further enhances the discrimination between similar wetland classes. A statistical analysis of the interferometric coherence and SAR backscattering variation of Canadian wetlands, which are ignored in the literature, is carried out using multi-temporal, multi-frequency, and multi-polarization SAR data. The study also examines the capability of compact polarimetry (CP) SAR data, which will be collected by the upcoming RADARSAT Constellation Mission (RCM) and will constitute the main source of SAR observation in Canada, for wetland mapping. The research in this dissertation proposes a methodology for wetland classification using the synergistic use of intensity, polarimetry, and interferometry features using a novel classification framework. Finally, this work introduces a novel model based on the deep convolutional neural network (CNN) for wetland classification that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PolSAR) imagery. The results of the proposed methods are promising and will significantly contribute to the ongoing efforts of conservation strategies for wetlands and monitoring changes. The approaches presented in this thesis serve as frameworks, progressing towards an operational methodology for mapping wetland complexes in Canada, as well as other wetlands worldwide with similar ecological characteristics

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl