Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

A multi-temporal hyperspectral camouflage detection and transparency experiment

Hyperspectral sensors are used to measure the electromagnetic spectrum in hundreds of narrow and contiguous spectral bands. The recorded data exhibits characteristic features of materials and objects. For tasks within the security and defense domain, this valuable information can be gathered remotely using drones, airplanes or satellites. In 2021, we conducted an experiment in Ettlingen, Germany, using a drone-borne hyperspectral sensor to record data of various camouflage setups. The goal was the inference of camouflage detection limits from typical hyperspectral data evaluation approaches for different scenarios. The experimental site is a natural strip of vegetation between two corn fields. Our main experiment was a camouflage garage that covered different target materials and objects. The distance between the targets and the roof of the camouflage garage was modified during the experiment. Together with the target variations, this was done to determine the material dependent detection limits and the transparency of the camouflage garage. Another experiment was carried out using two different types of camouflage nets in various states of occlusion by freshly cut vegetation. This manuscript contains a detailed experiment description, as well as, the first results of the camouflage transparency and occlusion experiment. We show that it is possible to determine the target inside the camouflage garage and that vegetation cover is not suitable additional camouflage for hyperspectral sensors

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions and have added another approach to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board foot volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated segmentation was performed with Trimble eCognition Developer\u27s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and the high resolution imagery was able to provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

LudVision Remote Detection of Exotic Invasive Aquatic Floral Species using Data from a DroneMounted Multispectral Sensor

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it’s reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. There have been ever­growing reports of invasive species affecting the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can have negative impacts on the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. To achieve this, we used images collected by a drone­mounted multispectral sensor. Due to the lack of publicly available data sets containing Ludwigia peploides, we had to create our own data set. We started by carefully studying all the available options. We first experimented with satellite images, but it was impossible to identify the targeted species due to their low resolution. Thus, we decided to use a drone­mounted multispectral sensor. Unfortunately, due to budget limitations, we could not acquire the highly specialized types of equipment that is more commonly used in remote sensing. However, we were confident that our setup would be enough to extract the species’ spectral signature, and that the higher resolution compared to satellites would allow us to use deep learning models to identify the species. The use of the drone allowed for better operational flexibility and to cover a large area. The multispectral sensor allowed us to leverage the information of two additional bands outside the visible spectrum. After visiting the study site multiple times and capturing data at various times of the day, we created a representative data set with different atmospheric conditions. After the data collection, we proceeded to the pre­processing and annotation steps to have a usable data set. In later stages, we proved that extracting the specie’s spectral signature from our data set is possible. This was a significant conclusion, as it proved that it is indeed possible to differentiate the species’ spectral signature with equipment that is not as advanced and specialized as the ones used in other studies. After having a data set, we focused on the next step, which was to develop and validate a method that would be able to identify Ludwigia p on our data. We decided on using semantic segmentation models to identify the species. Given that we only have two additional bands compared to traditional RGB images, we could not approach the problem as a standard remote sensing spectroscopy problem. By using semantic segmentation models, we can leverage both the capabilities of these models to recognize objects and the multispectral nature of our data. Fundamentally, the model has the same behavior as usual but has access to the information of two additional bands.We started by using an existing state­of­the­art semantic segmentation model adapted to handle our data. After doing some initial tests and establishing a baseline, we proposed and implemented some changes to the existing model. The goal of the modifications was to create a model with lower training times and better performance in detecting Ludwigia p. at high altitudes. The result is a new model better suited to our data and application. Our model is faster when it comes to training time while maintaining similar performance and has a slight performance increase in high­altitude images.O sensoriamento remoto é o processo de detetar e monitorizar as características físicas de uma área, medindo à distância a sua radiação refletida e emitida. É amplamente utilizado para monitorizar ecossistemas, principalmente tendo em vista a sua preservação. Há cada vez mais casos de espécies invasoras que afetam o equilíbrio natural dos ecossistemas. As espécies exóticas invasoras têm um impacto crítico quando introduzidas em novos ecossistemas e podem levar à extinção de espécies nativas. Neste estudo, focamo­nos na Ludwigia peploides, considerada pela União Europeia como uma espécie aquática invasora. A sua presença pode ter impactos negativos no ecossistema circundante e nas atividades humanas, como agricultura, pesca e navegação. O nosso objetivo foi desenvolver um método para identificar a presença da espécie. Para isso, usámos imagens capturadas por um sensor multiespectral montado num drone. Devido à falta de conjuntos de dados disponíveis publicamente contendo Ludwigia peploides, tivemos que criar nosso próprio conjunto de dados. Começámos por cuidadosamente estudar todas as opções disponíveis. Primeiro fizemos experiências com imagens de satélite, mas foi impossível identificar a espécie­alvo devido à baixa resolução das imagens. Assim, decidimos usar um sensor multiespectral montado num drone. Infelizmente, devido a limitações orçamentais, não conseguimos adquirir os tipos de equipamentos altamente especializados que são tipicamente usados em sensoriamento remoto. No entanto, estávamos confiantes de que nossa configuração seria suficiente para extrair a assinatura espectral da espécie, e que a alta resolução das nossas imagens comparadas com de satélite, nos permitiria usar modelos de aprendizagem profunda para identificar as espécies. O uso do drone permitiu uma maior flexibilidade operacional e cobertura de uma grande área. O sensor multiespectral permitiu­nos alavancar as informações de duas bandas adicionais fora do espectro visível. Depois de visitar o local de estudo várias vezes e capturar dados em vários momentos do dia, criámos um conjunto de dados representativo com diferentes condições atmosféricas. Após a captura de dados, procedeu­se às etapas de pré­processamento e anotação para ter um conjunto de dados utilizável. Em etapas posteriores, provámos que é possível extrair dos nossos dados a assinatura espectral da espécie. Esta foi uma conclusão significativa, pois comprovou que de fato é possível diferenciar a assinatura espectral da espécie com equipamentos não tão avançados e especializados quanto os utilizados noutros estudos. Depois de termos um conjunto de dados, focamo­nos no próximo passo, que foi desenvolver e validar um método que fosse capaz de identificar Ludwigia p. nos nossos dados. Decidimos usar modelos de segmentação semântica para identificar as espécies. Dado que temos apenas duas bandas adicionais em comparação com as imagens RGB tradicionais, não poderíamos abordar o problema como um problema de espectroscopia de sensoriamento remoto padrão. Ao usar modelos de segmentação semântica, podemos aproveitar não só os recursos desses modelos para reconhecer objetos, mas também a natureza multiespectral de nossos dados. Fundamentalmente, o modelo tem o mesmo comportamento usual, mas tem acesso às informações de duas bandas adicionais. Começamos por usar um modelo de segmentação semântica estado­da­arte existente, que foi adaptado para lidar com nossos dados. Depois de fazer alguns testes iniciais e estabelecer uma base de comparação, propusemos e implementámos algumas modificações ao modelo existente. O objetivo das modificações foi criar um modelo com menores tempos de treino e melhor desempenho na deteção de Ludwigia p. em altitudes elevadas. O resultado é um novo modelo mais adequado aos nossos dados e aplicação. O nosso modelo é mais rápido no que diz respeito ao tempo de treino, mantendo desempenho semelhante, apresentando mesmo um ligeiro aumento de desempenho em imagens de alta altitude

Early mapping of industrial tomato in Central and Southern Italy with Sentinel 2, aerial and RapidEye additional data

Timely crop information, i.e. well before harvesting time and at first stages of crop development, can benefit farmers and producer organizations. The current case study documents the procedure to deliver early data on planted tomato to users, showing the potential of Sentinel 2 to map tomato at the very beginning of the crop season, which is a challenging task. Using satellite data, integrated with ground and aerial data, an initial estimate of area planted with tomato and early tomato maps were generated in seven main production areas in Italy. Estimates of the amount of area planted with tomato provided similar results either when derived from field surveys or from remote sensing-based classification. Tomato early maps showed a producer accuracy > 80% in seven cases out of nine, and a user accuracy > 80% in five cases out of nine, with differences attributed to the varying agricultural characteristics and environmental heterogeneity of the study areas. The additional use of aerial data improved producer accuracy moderately. The ability to identify abrupt growth changes, such as those caused by natural hazards, was also analysed: Sentinel 2 detected significant changes in tomato growth between a hailstorm-affected area and a control area. The study suggests that Sentinel 2, with enhanced spectral capabilities and open data policy, represents very valuable data, allowing crop monitoring at an early development stage

Efficient multitemporal change detection techniques for hyperspectral images on GPU

Hyperspectral images contain hundreds of reflectance values for each pixel. Detecting regions of change in multiple hyperspectral images of the same scene taken at different times is of widespread interest for a large number of applications. For remote sensing, in particular, a very common application is land-cover analysis. The high dimensionality of the hyperspectral images makes the development of computationally efficient processing schemes critical. This thesis focuses on the development of change detection approaches at object level, based on supervised direct multidate classification, for hyperspectral datasets. The proposed approaches improve the accuracy of current state of the art algorithms and their projection onto Graphics Processing Units (GPUs) allows their execution in real-time scenarios

Characterizing Spatiotemporal Patterns of White Mold in Soybean across South Dakota Using Remote Sensing

Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to previous reports, environmental conditions corresponding to high yield potential are most conducive for white mold development. These conditions include cool temperature (12-24 °C), continued wet and moist conditions (70-120 h) generally resulting from rain, but the disease development requires the presence of a susceptible soybean variety. To better understand white mold development in the field, there is a need to investigate its spatiotemoral characteristics and provide accurate estimates of the damages that white mold may cause. Current and accurate data about white mold are scarce, especially at county or larger scale. Studies that explored the characteristics of white mold were generally field oriented and local in scale, and when the spectral characteristics were investigated, the authors used spectroradiometers that are not accessible to farmers and to the general public and are mostly used for experimental modeling. This study employed free remote sensing Landsat 8 images to quantify white mold in South Dakota. Images acquired in May and July were used to map the land cover and extract the soybean mask, while an image acquired in August was used to map and quantify white mold using the random forest algorithm. The land cover map was produced with an overall accuracy of 95% while white mold was mapped with an overall accuracy of 99%. White mold area estimates were respectively 132 km2, 88 km2, and 190 km2, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties. This study also explored the spatial characteristics of white mold in soybean fields and its impact on yield. The yield distribution exhibited a significant positive spatial autocorrelation (Moran’s I = 0.38, p-value \u3c 0.001 for Moody field, Moran’s I = 0.45, p-value \u3c 0.001, for Marshall field) as an evidence of clustering. Significant clusters could be observed in white mold areas (low-low clusters) or in healthy soybeans (high-high clusters). The yield loss caused by the worst white mold was estimated at 36% and 56% respectively for the Moody and the Marshall fields, with the most accurate loss estimation occurring between late August and early September. Finally, this study modeled the temporal evolution of white mold using a logistic regression analysis in which the white mold was modeled as a function of the NDVI. The model was successful, but further improved by the inclusion of the Day of the Year (DOY). The respective areas under the curves (AUC) were 0.95 for NDVI and 0.99 for NDVI+DOY models. A comparison of the NDVI temporal change between different sites showed that white mold temporal development was affected by the site location, which could be influenced by many local parameters such as the soil properties, the local elevation, management practices, or weather parameters. This study showed the importance of freely available remotely sensed satellite images in the estimation of crop disease areas and in the characterization of the spatial and temporal patterns of crop disease; this could help in timely disease damage assessment