Vegetation characterization through the use of precipitation-affected SAR signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.1010FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50943-

Worldwide Research on Land Use and Land Cover in the Amazon Region

Land cover is an important descriptor of the earth’s terrestrial surface. It is also crucial to determine the biophysical processes in global environmental change. Land-use change showcases the management of the land while revealing what motivated the alteration of the land cover. The type of land use can represent local economic and social benefits, framed towards regional sustainable development. The Amazon stands out for being the largest tropical forest globally, with the most extraordinary biodiversity, and plays an essential role in climate regulation. The present work proposes to carry out a bibliometric analysis of 1590 articles indexed in the Scopus database. It uses both Microsoft Excel and VOSviewer software for the evaluation of author keywords, authors, and countries. The method encompasses (i) search criteria, (ii) search and document compilation, (iii) software selection and data extraction, and (iv) data analysis. The results classify the main research fields into nine main topics with increasing relevance: ‘Amazon’, ‘deforestation’, ‘remote sensing’, ‘land use and land cover change’, and ‘land use’. In conclusion, the cocitation authors’ network reveals the development of such areas and the interest they present due to their worldwide importance

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

RICE YIELD ESTIMATION USING REMOTE SENSING AND CROP SIMULATION MODEL IN NALGONDA DISTRICT, TELANGANA

A study on “Rice yield estimation using Remote Sensing and crop simulation model in Nalgonda district, Telangana” was carried out during kharif, 2021. Precise and real-time agricultural yield data at the national, international and regional levels is becoming increasingly crucial for global food security. Crop yield forecasting could be very useful in advanced crop planning, strategy creation, and management. Because of the importance of yield prediction in food security, the present study used the APSIM-ORYZA model and remote sensing to estimate rice yield. The core objective of this study was to develop a method to integrate remotely sensed data and APSIM model for rice yield estimation in Nalgonda district, Telangana. This study includes mapping of rice growing areas and execution of APSIM model, followed by integration of remote sensing and crop simulation model for rice yield prediction and verification using government statistics. Based on stratification, two villages, Telakantigudem from Kangal mandal and Mallaram village from Kattangoor mandal in Nalgonda district were selected and ten fields from each village were chosen for the study to collect the measured LAI values with the help of ceptometer in the fields and the crop management data from the respected farmers. Crop classification was performed on Sentinel-1 and Sentinel-2 time series data using a Random Forest (RF) classifier and ground reference points collected from field surveys in the Google Earth Engine platform. The results demonstrated an overall accuracy of 92% and a kappa coefficient of 0.85, and rice area was validated with the crop coverage report (kharif, 2021) provided by the Department of Agriculture (DOA), Telangana state showed a relative variation of -0.16%. Remote sensing products like VV, VH AND VH/VV from Sentinel-1 and NIR, Red and NDVI from Sentinel-2 were derived using GEE and were calibrated with the measured LAI data collected from farmers’ fields. The result showed that there was a significant relation (R2=0.78) between NDVI and field LAI and hence it was considered for integration with the crop model output. Maps were derived showing spatial variation in crop extent, and leaf area index (LAI), which are crucial in yield assessment. Execution of APSIM-ORYZA model was done using the weather parameters, soil parameters, genetic coefficients and crop management data. The evaluation of the model with simulated yield and observed yield in the farmers’ fields showed linear regression of R2 = 0.79, root mean square error (RMSE)=804 kg ha-1 and mean absolute error (MAE)=728 kg ha-1. The overall spatially averaged model yield for the district showed 4925 kg ha-1 which is deviated by 2% from the average yield in the government statistics with 5024 kg ha-1. The study showed that by assimilation of remotely sensed data with the crop models, crop yields before harvest could be successfully predicted

Micro-topography associated to forest edges

Forest edges are often defined as the discontinuity between the forest habitat and an adjacent open habitat, thus they are based on a clear difference in the structure of the dominant vegetation. However, beside this very general definition, in the field we can observe a large diversity of edges, with often different kinds of micro-topography features: bank, ditch, stone wall, path, etc. As these elements are rather common in many temperate forest edges, it seems important to start to characterize them more clearly and with consistency. From a set of observations in south-western France, we build a first typology of the micro-topographic elements associated to forest edges. For each of them we describe the process, natural or human induced, at their origin, and according to the literature available, we identify some of their key ecological roles. Banks, generated by the differential erosion between forest and crops along slopes, are especially analyzed since they are the most common micro-topographic element in our region. It offers many micro-habitat conditions in the soil used by a wide range of species, notably by several bee species. More research is required to study in details the importance of such micro-topographic elements

Semi-supervised text classification framework : an overview of Dengue landscape factors and satellite earth observation

In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context

Métodos de classificação de imagens de satélite para delineamento de banhados

As Áreas Úmidas (AUs) são ecossistemas de importância global, que apresentam altos níveis de diversidade ecológica e produtividade primária e secundária. Os Banhados são um tipo de AU, característicos nos estados do Sul do Brasil, no Uruguai e na Argentina. O delineamento e classificação desses ecossistemas é uma tarefa árdua, dada as características estruturais hidrológicas, de solos, de cobertura vegetal e espectrais. No estado Rio Grande do Sul os Banhados são considerados Áreas de Preservação Permanente, porém, não há um inventário e tampouco um delineamento desses ambientes. Deste modo, o objetivo destatese é comparar diferentes métodos baseados em sensoriamento remoto ativo e passivo e aprendizado de máquina(AP)para o delineamento de Banhados. Para isto, utilizamos três abordagens: i) aplicação de índices espectrais de sensoriamento remoto e árvore de decisão; ii) integração de imagens SAR de dupla e quádrupla polarização em bandas C e L e árvore de decisão; e, iii) análise multisensor (ativo e passivo), Geobia e diferentes classificadores. Nossos resultados mostram que os índices espectrais de sensoriamento remoto apresentaram acurácias entre 77,9% e 95,9%; a aplicação de imagens SAR resultou em acurácias entre 56,1% e 72,9%, ambos pelo algoritmo Árvore de Decisão. Para a abordagem multisensor utilizando Geobia e diferentes classificadores, as acurácias variaram entre 95,5% e 98,5%, sendo que, o k-NN foi o algoritmo que apresentou maior acurácia entre os modelos avaliados, demonstrando o potencial da análise multisensor (ativo e passivo) e doaprendizado de máquinapara o delineamento e classificação de Banhados. Adotamos como estudo de caso um Banhado localizado no Sul do Brasil, porém recomendamos que devido as semelhanças hidrológicas, estruturais e espectrais desses ambientes, essas metodologias possam ser aplicadas em outras áreas de Banhados (marshes).Wetlands are ecosystems of global importance, with high levels of ecological diversity and primary and secondary productivity.Marshes are a type of wetland characteristic of the southernBrazil, Uruguay and Argentina.The delineationand classification of these ecosystems is an arduous task, given the hydrological structure, soil, vegetation and spectral characteristics.In the Rio Grande do Sul state, marshesare considered Permanent Preservation Areas, however, there is no inventory and no delineationof these environments.Thus, the aim of this thesis is to compare different active and passive remote sensing based methodsand machine learningfor the delineationof marshes. For this, we use three approaches: i) application of spectral indices of remote sensing and decision tree; ii) integration of dual and quad-poll SAR images in C and L-bands and decision tree, and iii) multisensor analysis (active and passive), Geobia and different classification methods. Our results show that the spectral indexes of remote sensing presented accuracy between 77.9% and 95.9%; the application of SAR images resulted in accuracy between 56.1% and 72.9%, both using the Decision Tree algorithm. For the multisensor approach using Geobia and different classifiers, the accuracy varied between 95.5% to 98.5%, k-NN was the algorithm that showed greater accuracy among the models evaluated, demonstrating the potential of the multisensor analysis (activeand passive) and machine learningfor marshesdelineation and classification. Our study was carried out in a marsh located in the southernBrazil, however due to the hydrological, structural and spectral similarities of these environments, the methodologies can be applied in other marshes area

Urban Growth and Its Impact on Urban Heat Sink and Island Formation in the Desert City of Dubai.

The rapid pace of urban growth in Dubai has attracted the attention of economists, environmentalists and urban planners. This thesis quantifies the extent of urbanisation within the Emirate since the discovery of oil and investigates the impacts of such growth on urban temperatures. The study used remotely-sensed imagery in the absence of publicly available data on city growth and microclimate. The study used a hybrid classification method and landscape metrics to capture historical urban forms, rates and engines of growth in the Emirate. Stepwise multiple regression analysis techniques were subsequently used to investigate the relationship between the rate and form of urbanisation and the intensity of the urban heat sink between 1990 and 2011. Local Climate Zones were then developed to specifically investigate the impacts of urban geometry variables and proximity to water on both urban heat sinks during the day-time and urban heat islands during the night. The study revealed a significant increase in urban area over time (1972-2011) with accelerated phases of growth, linked to local and global economic conditions, occurring during specific periods. Physical urban growth has now outpaced population growth, indicating urban sprawl. This growth has occurred at the expense of sand and has included a significant increase in vegetation and water bodies unlike other desert cities in the Gulf region. The results demonstrated that urban growth has promoted a heat sink effect during daytime and that all urban land use types contributed to this effect. Urban albedo was not responsible for the daytime urban heat sink; other factors including the specific heat capacity of urban materials, urban geometry and proximity to the Gulf were mainly responsible. Furthermore, increases in vegetation cover and impervious surface cover over time have contributed to the daytime (morning) urban heat sink. At night-time, urban geometry and proximity to the Gulf were the major influences upon the formation of urban heat islands. This research contributes to better understanding of urbanisation in desert cities as demonstrated through Dubai, revealing previously unknown spatiotemporal variations in urban areas across the city through the use of a time-series of satellite images. The findings provide new insights into the impacts of land cover, land use, proximity to water and urban geometry on the formation of urban heat sinks and urban heat islands in the desert environment

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability

Celebrating 25 Years of World Wetlands Day

The purpose of this Special Issue is to celebrate 25 years of “World Wetlands Day”. There is no other ecosystem that has its very own Ramsar Convention or such a challenge impacting ecosystem sustainability. Papers for this Special Issue provide an overview of wetland status and function within different regions of the world. The papers in this Special Issue of Land consist of three review papers, ten research articles and one perspective paper. Edward Maltby’s review paper provides us with an overview of the paradigm shift of how we value and assess wetlands over time. Ballut-Dajud et al. provide us with a worldwide perspective on factors affecting wetland loss. Finally, Jan Vymazal provides us with a historical overview of the development of water quality treatment wetlands in Europe and North America. The research papers can be grouped into four groups: 1) use of remote sensing to analyze stability and dynamic factors affecting wetlands; 2) factors affecting the wetlands’ ability to store carbon; 3) assessment of wetlands effect on water quality; and 4) understanding historical use and value of wetlands, farmer’s attitudes about wetland management, and how we can value wetland ecosystem services. Finally, Bryzek et al. remind us that, as wetland researchers and managers, we should minimize damage to wetlands even through field monitoring work