Novel parameters for evaluating the Spatial and Thematic accuracy of land cover maps.

Se proponen novedosas fórmulas para evaluar la certeza de la cartografí

Spatial and thematic assessment of object- based forest stands delineation using OFA-matrix

Validación de la cartografía generada del terreno a partir de una nuevo sistema de validación propuest

Evaluation of remote sensing methods for continuous cover forestry

The overall aim of the project was to investigate the potential and challenges in the application of high spatial and spectral resolution remote sensing to forest stands in the UK for Continuous Cover Forestry (CCF) purposes. Within the context of CCF, a relatively new forest management strategy that has been implemented in several European countries, the usefulness of digital remote sensing techniques lie in their potential ability to retrieve parameters at sub-stand level and, in particular, in the assessment of natural regeneration and light regimes. The idea behind CCF is the support of a sustainable forest management system reducing disturbance of the forest ecosystem and encouraging the use of more natural methods, e.g. natural regeneration, for which the light environment beneath the forest canopy plays a fundamental role.The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Park (lat. 56°10' N, long. 4° 23' W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterized by their different light regimes, were established within the area for the measurement of forest variables using a forest inventory approach and hemispherical photography. The remote sensing data available for the study consisted of Landsat ETM+ imagery, a small footprint multi-return lidar dataset over the study area, Airborne Thematic Mapper (ATM) data, and aerial photography with same acquisition date as the lidar data.Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phenological change as a factor to consider for future acquisitions of remotely sensed imagery. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). It can be concluded that no single date is ideal for discriminating the species studied (early summer was best) and that a combination of two or three datasets covering their phenological cycles is optimal for the differentiation. Although the approaches used helped to characterize the forest species, especially to the discrimination between spruces, larch and the deciduous oak species, further work is needed in order to define an optimum approach to discriminate between spruce species (e.g. Sitka spruce and Norway spruce) for which spectral responses are very similar. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended.Lidar, ATM, and aerial photographic datasets were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and tree heights from multiple return lidar data, the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, followed by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree heights were estimated for all sample plots. The results showed that lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38 m.This study assessed the utility of an object-oriented approach for deciduous and coniferous crown delineation, based on small-footprint, multiple return lidar data, high resolution ATM imagery, and aerial photography. Special emphasis in the analysis was made in the fusion of aerial photography and lidar data for tree crown detection and classification, as it was expected that the high vertical accuracy of lidar, combined with the high spatial resolution aerial photography would render the best results and would provide the forestry sector with an affordable and accurate means for forest management and planning. Most of the field surveyed trees could be automatically and correctly detected, especially for the spruce and larch plots, but the complexity of the deciduous plots hindered the tree recognition approach, leading to poor crown extent and gap estimations. Indicators of light availability were calculated from the lidar data by calculation of laser hit penetration rates and percentage canopy cover. These results were compared to estimates of canopy openness obtained from hemispherical pictures for the same locations.Finally, the synergistic benefits of all datasets were evaluated and the forest structural variables determined from remote sensing and hemispherical photography were examined as indicators of light availability for regenerating seedlings

A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation

Inland Marsh (IM) is a type of wetland characterized by the presence of non-woody plants as grasses, reeds or sedges, with a water surface smaller than 25% of the area. Historically, these areas have been suffering impacts related to pollution by urban, industrial and agrochemical waste, as well as drainage for agriculture. The IM delineation allows to understand the vegetation and hydrodynamic dynamics and also to monitor the degradation caused by human-induced activities. This work aimed to compare four machine learning algorithms (classification and regression tree (CART), artificial neural network (ANN), random forest (RF), and k-nearest neighbors (k-NN)) using active and passive remote sensing data in order to address the following questions: (1) which of the four machine learning methods has the greatest potential for inland marshes delineation? (2) are SAR features more important for inland marshes delineation than optical features? and (3) what are the most accurate classification parameters for inland marshes delineation? To address these questions, we used data from Sentinel 1A and Alos Palsar I (SAR) and Sentinel 2A (optical) sensors, in a geographic object-based image analysis (GEOBIA) approach. In addition, we performed a vectorization of a 1975 Brazilian Army topographic chart (first official document presenting marsh boundaries) in order to quantify the marsh area losses between 1975 and 2018 by comparing it with a Sentinel 2A image. Our results showed that the method with the highest overall accuracy was k-NN, with 98.5%. The accuracies for the RF, ANN, and CART methods were 98.3%, 96.0% and 95.5%, respectively. The four classifiers presented accuracies exceeding 95%, showing that all methods have potential for inland marsh delineation. However, we note that the classification results have a great dependence on the input layers. Regarding the importance of the features, SAR images were more important in RF and ANN models, especially in the HV, HV + VH and VH channels of the Alos Palsar I L-band satellite, while spectral indices from optical images were more important in the marshes delineation with the CART method. In addition, we found that the CART and ANN methods presented the largest variations of the overall accuracy (OA) in relation to the different parameters tested. The multi-sensor approach was critical for the high OA values found in the IM delineation (> 95%). The four machine learning methods can be accurately applied for IM delineation, acting as an important low-cost tool for monitoring and managing these environments, in the face of advances in agriculture, soil degradation and pollution of water resources due to agrochemical dumping

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

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

A method for continuous sub-annual mapping of forest disturbances using optical time series

Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra- and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91±0.08 and 0.81±0.07 (with 95% confidence intervals) for the disturbed areas, respectively

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry