Mapping Forest Health Using Spectral And Textural Information Extracted From Spot-5 Satellite Images

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management

Sensitivity of high-resolution satellite sensor imagery to regenerating forest age and site preparation for wildlife habitat analysis

In west-central Alberta increased landscape fragmentation has lead to increased human use, having negative effects on wildlife such as the grizzly bear (Ursus arctos L.). Recently, grizzly bears in the Foothills Model Forest were found to select clear cuts of different age ranges as habitat and selected or avoided certain clear cuts depending on the site preparation process employed. Satellite remote sensing offers a practical and cost-effective method by which cut areas, their age, and site preparation activities can be quantified. This thesis examines the utility of spectral reflectance of SPOT-5 pansharpened imagery (2.5m spatial resolution) to identify and map 44 regenerating stands sampled in August 2005. Using object based classification with the Normalized Difference Moisture Index (NDMI), green, and short wave infrared (SWIR) bands, 90% accuracy can be achieved in the detection of forest disturbance. Forest structural parameters were used to calculate the structural complexity index (SCI), the first loading of a principal components analysis. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (R2=0.74). The best window size for the texture measures was 5x5, indicating that this is a measure only detectable at a very high spatial resolution. Age classes of these cut blocks were analysed using linear discriminant analysis and best separated (82.5%) with the SWIR and green spectral bands, second order correlation under a 25x25 window, and the predicted SCI. Site preparation was best classified (90.9%) using the NDMI and homogeneity texture under a 5x5 window. Future applications from this research include the selection of high probability grizzly habitat for high spatial resolution imagery acquisition for detailed mapping initiatives

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

Développement d’une méthode de télédétection pour l’identification d’espèces exotiques envahissantes dans l’agglomération de Québec

Les espèces exotiques envahissantes végétales (EEEv) sont actuellement considérées comme étant à l’origine de plusieurs types d’impacts négatifs dont la perte de la biodiversité et l’altération du fonctionnement des écosystèmes. Dans l’agglomération de Québec, la présence de plusieurs EEEv et les informations partielles sur leur distribution territoriale limitent la mise en place de stratégies efficaces de contrôle et d’éradication. Ces données sur la distribution territoriale peuvent être acquises à partir des inventaires in situ. Cependant, ces derniers nécessitent beaucoup de temps surtout dans les milieux envahis par plusieurs EEEv en même temps tels que les milieux urbains. Ces inventaires ne sont également pas adaptés financièrement et techniquement, lorsqu’il s’agit de grandes étendues ou lorsque les conditions topographiques ne sont pas favorables. La télédétection pourrait être utilisée pour contrer ces limites afin de cartographier les EEEv, suivre leur prolifération et intervenir rapidement. Le but de cette étude consistait donc à élaborer une méthode de cartographie multi-espèces par télédétection de cinq EEEv terrestres présentes dans l’agglomération de Québec, à savoir la renouée du Japon (Fallopia japonica), le phragmite (Phragmites australis), la berce du Caucase (Heracleum mantegazzianum), le nerprun bourdaine (Frangula alnus) et le nerprun cathartique (Rhamnus cathartica). L’approche méthodologique consistait à réaliser une cartographie mono-date et multi-date à l’aide d’images satellitaires WorldView-3 acquises en été, SPOT-7 et GeoEye-1 acquises en automne. Une classification orientée-objet combinée à des méthodes d’apprentissage automatique non paramétriques, à savoir Support Vector Machine (SVM), Random Forest (RF) et Extreme Gradient Boosting (XGBoost) a été utilisée afin de produire des probabilités de présence de ces EEEv. La cartographie des nerpruns a été réalisée à part car leur faible présence sur la zone d’étude et leur distribution sous-couvert à faible densité a nécessité un ajout de l’image GeoEye-1 et un paramétrage des méthodes différent de celui utilisé pour les trois premières EEEv. La combinaison des images WorldView-3 et SPOT-7 a permis d’atteindre d’excellentes performances pour les trois premières EEEv, avec un coefficient Kappa de 0,85 et une précision globale de 91 % en utilisant RF. Les performances individuelles des classes basées sur l’indicateur F1-score ont montré que la renouée du Japon est mieux détectée (F1-score maximal = 0,95), que la berce du Caucase (F1-score maximal = 0,91) et le phragmite (F1-score maximal = 0,87). La classification multi-date des nerpruns est, par contre, moins performante par rapport à celle des autres espèces avec un coefficient Kappa égal à 0,72, une précision globale de 83 % et F1-score maximal égal 0,62. Cette étude montre la possibilité de cartographie et suivi des principales EEEv selon une approche multi-date. Les limites de cette étude, à savoir la faible quantité de données de référence d’EEEv, les coûts élevés d’acquisition et la faible disponibilité des images satellitaires à très haute résolution spatiale ainsi que la distribution des nerpruns en sous-couvert (dans notre zone d’étude) pourraient être réduites en utilisant des images plus accessibles en combinaison avec les techniques de super-résolution. Les données LiDAR à haute densité pourraient également être intégrées à l’imagerie optique afin d’améliorer les performances de cartographie des nerpruns

Efficiency of remote sensing tools for post-fire management along a climatic gradient

P. 553-562Forest managers require reliable tools to evaluate post-fire recovery across different geographic/climatic contexts and define management actions at the landscape scale, which might be highly resource-consuming in terms of data collection. In this sense, remote sensing techniques allow for gathering environmental data over large areas with low collection effort. We aim to assess the applicability of remote sensing tools in post-fire management within and across three mega-fires that occurred in pine fire-prone ecosystems located along an Atlantic-Transition-Mediterranean climatic gradient. Four years after the wildfires, we established 120 2x2m plots in each mega-fire site, where we evaluated: (1) density of pine seedlings, (2) percentage of woody species cover and (3) percentage of dead plant material cover. These variables were modeled following a Bayesian Model Averaging approach on the basis of spectral indices and texture features derived from WorldView-2 satellite imagery at 2 m spatial resolution. We assessed model interpolation and transferability within each mega-fire, as well as model extrapolation between mega-fires along the climatic gradient. Texture features were the predictors that contributed most in all cases. The woody species cover model had the best performance regarding spatial interpolation and transferability within the three study sites, with predictive errors lower than 25% for the two approaches. Model extrapolation between the Transition and Mediterranean sites had low levels of error (from 6% to 19%) for the three field variables, because the landscape in these areas is similar in structure and function and, therefore, in spectral characteristics. However, model extrapolation from the Atlantic site achieved the weakest results (error higher than 30%), due to the large ecological differences between this particular site and the others. This study demonstrates the potential of fine-grained satellite imagery for land managers to conduct post-fire recovery studies with a high degree of generality across different geographic/climatic contexts.S

A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

Remote Sensing-Based Biomass Estimation

Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms