Object-based classification of vegetation cover typologies in wetland, integrating optical images and SAR

Delinear com precisão os limites das Áreas Úmidas (AUs) e os padrões de cobertura vegetal é um passo essencial para a rápida avaliação e gestão destes ecossistemas. A Análise de Imagens Baseada em Objeto (Object-Based Image Analysis - OBIA) a partir de aprendizado de máquina e da integração de dados ópticos e de radar apresentam vantagens em relação a outras técnicas no mapeamento da cobertura vegetal nos ecossistemas de AUs. Este estudo tem como objetivo classificar tipologias de cobertura vegetal em áreas úmidas, integrando imagens ópticas e SAR dos satélites Sentinel-1 e 2A e o algoritmo Random Forest à classificação OBIA, utilizando como estudo de caso o Banhado Grande, localizado no Rio Grande do Sul. Como resultados, as polarizações VH e VV do Sentinel-1 obtiveram a maior relevância na classificação (18,6%). Entre as bandas ópticas as maiores relevâncias ocorreram para as bandas Borda Vermelha e Infravermelho Médio. A partir dos atributos ópticos, a classificação obteve acurácia de 86,2%. Quando inseridos os atributos SAR mais importantes, a acurácia aumentou para 91,3%. A classe Macrófitas Emergentes (ME), correspondente à espécie Scirpus giganteus, alcançou a melhor acurácia (91%), com área estimada em 1.507 ha. Concluímos que a integração de imagens aliada ao método de classificação possibilitou identificar e delimitar a extensão das tipologias vegetais e a área total do ecossistema. Os resultados acurados demostram que esta abordagem metodológica pode ser expandida para outras áreas úmidas palustres subtropicais.Accurately mapping the boundaries of wetlands and patterns of vegetation cover is an essential step for rapid assessment and management of wetlands. The Object-Based Image Analysis (OBIA) as from machine learning and fusion of optical and radar data has advantages over other techniques for mapping vegetation cover in wetlands ecosystems. This study aims to classify vegetation cover typologies in wetlands, integrating optical and SAR images from the Sentinel-1 and 2A satellites and the Random Forest algorithm in OBIA classification, using Banhado Grande, located in the Rio Grande do Sul as a case study. As a result, the VH and VV polarizations of Sentinel-1 obtained the highest relevance in the classification (18.6%). Among the optical bands, the greatest relevance occurred for the Red Edge and Medium Infrared bands. From the optical attributes, the classification obtained an accuracy of 86.2%. When the most important SAR attributes were inserted, the accuracy increased to 91.3%. The Emergent Macrophyte (ME) class, corresponding to the species Scirpus giganteus, achieved the best accuracy of the classifier (91%), with an estimated area of 1,507 ha. We conclude that the integration of images combined with the classification method made it possible to delimit the extent of vegetation typologies and the total area of the ecosystem. Accurate results show that this methodological approach can be expanded to other subtropical palustrine wetlands

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R2) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

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

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools