Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

Quantifying the urban forest environment using dense discrete return LiDAR and aerial color imagery for segmentation and object-level biomass assessment

The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized. In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management

다중 규모 LiDAR 데이터를 활용한 도시생태계 구조 및 연결성 평가

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2021.8. 송영근.Integrated multiscale light detection and ranging (LiDAR) datasets are required for managing urban ecosystems because 1) LiDAR datasets can represent various spatial structures across the urban landscape and 2) the multitemporal LiDAR approach can derive the changes of urban landscape structures. This dissertation aimed to find the various spatiotemporal availabilities (i.e., from the tree-level spatial scale to the city-level regional scale with the multitemporal approach) of LiDAR or laser scanning (LS) datasets for monitoring urban ecosystems in the following three chapters. Chapter 2: Collecting tree inventory data in urban areas is important for managing green areas. Surveying using airborne laser scanning (ALS) is effective for collecting urban tree structures but less efficient regarding the economic costs and its operation. Terrestrial laser scanning (TLS), and mobile laser scanning (MLS) datasets could have the potential in complementing those of ALS in the respect to efficiency. However, to the best of my knowledge, there were limited studies for seeking the similarities and variations among the canopy metrics derived from various LiDAR platforms. In Chapter 2, I compared structural canopy metrics among ALS, TLS, and MLS datasets in the urban parks. The purpose of Chapter 2 was to test whether the estimates of tree metrics differed depending on single or clustered trees and to test whether the errors in LiDAR-derived metrics were related to the tree structures. Small, urban parks were selected for surveying trees using the three LiDAR platforms. The ALS datasets were acquired on 14 May, 2017. The TLS and MLS datasets were acquired from 10–11 May, 2017, and 21–25 April, 2020, respectively. The tree point clouds were classified into single and clustered trees. The structural metrics were compared in each pair (i.e., ALS and TLS, ALS and MLS, and TLS and MLS pairs). The heights related metrics (e.g., percentile heights and the distribution of the heights values), the complexity metric (e.g., the Rumple index) and area were calculated for comparisons. The root mean square error (RMSE), bias, and the Pearson’s correlation coefficient (r) were calculated to evaluate the difference in each metric among the LiDAR platforms. The results showed that ZMAX, max and mean CHM, and area showed good consistencies (RMSE% 0.900). Especially, the biases of CHM-derived metrics did not present significant differences (p > 0.05) regardless of single or clustered trees. Moreover, the biases from the comparisons in each pair showed linear relations with the tree heights and vertical canopy complexity (i.e., Pearson’s correlation coefficient showed significant; r >0.29, p < 0.05). My results could be references when combining multiple LiDAR systems to estimate the canopy structures of urban park areas. Chapter 3: Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional (3D) changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal ALS datasets enable me to quantify the vertical and lateral growth of trees across a landscape scale. The goal of Chapter 3 is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, I generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests. Chapter 4: Although many studies have considered urban structure when investigating urban ecological networks, few have considered the 3D structure of buildings as well as urban green spaces. In Chapter 4, I examined an urban ecological network using the 3D structure of both green spaces and buildings. Using breeding-season bird species observations and ALS data collected, I assessed the influence of 3D structural variables on species diversity. I used correlation analyses to determine if vertical distribution, volume, area, and height of both buildings and vegetation were related to bird species diversity. Then I conducted circuit theory-based current flow betweenness centrality (CFBC) analysis using the LiDAR-derived structural variables. I found that the volumes of buildings and 8–10 m vegetation heights were both highly correlated with species richness per unit area. There were significant differences between 2D and 3D connectivity analysis using LiDAR-derived variables among urban forest patches, boulevards, and apartment complexes. Within urban forest patches and parks, 3D CFBC represented canopy structural characteristics well, by showing high variance in spatial distributions. The 3D CFBC results indicated that adjacent high-rise buildings, dense apartment complexes, and densely urbanized areas were isolated, as characterized by low centrality values, but that vegetation planted in open spaces between buildings could improve connectivity by linking isolated areas to core areas. My research highlights the importance of considering 3D structure in planning and managing urban ecological connectivity. In this dissertation, the availability of integrated multiscale LiDAR datasets was found via three standalone studies. It was revealed that 3D information could enhance the quality of urban landscape monitoring and ecological connectivity analysis by elaborately explaining spatial structures. However, the spatiotemporal scales of each standalone study were limited to the city scale and to five years. The recently launched Global Ecosystem Dynamics Investigation (GEDI) would help to solve these limitations. Furthermore, the GEDI dataset could help researchers understand the relationship between ecosystem structures and their functions.본 학위논문은 다양한 시공간 스케일에서 도시생태계 모니터링을 위한 LiDAR 데이터의 활용과 생태적 의미 도출에 관한 내용을 다룬다. LiDAR란 Light Detection and Ranging의 약어로, LiDAR 센서에서 발사된 레이저가 대상에 도달한 뒤 반사되어 돌아오는 레이저의 세기와 시간을 계산하여 대상의 위치 정보를 3차원 점군 데이터로 변환해주는 능동형 원격탐사 도구이다. LiDAR 원격탐사 도구의 등장으로 자연과 도시의 3차원 공간정보의 취득이 기능해짐에 따라, 서식지의 3차원 공간정보와 생물 종 사이의 관계 도출, 시계열 LiDAR 데이터를 활용한 녹지 모니터링 연구 등이 이뤄지고 있다. 또한 항공 LiDAR(ALS), 지상 LiDAR(TLS), 이동형 LiDAR(MLS) 등 다양한 LiDAR 시스템의 개발로 연구 목적에 알맞은 시공간 해상도의 3차원 공간정보를 취득할 수 있게 되었다. 본 학위논문에서는 도시녹지를 대상으로 LiDAR 원격탐사 도구의 다양한 시공간 스케일 적용 측면에서, Chapter 2 항공, 지상, 이동형 LiDAR 시스템 사이 수목구조관련 변수들의 일치성 평가, Chapter 3 시계열 분석을 통한 도시녹지 동태 모니터링, Chapter 4 도시의 생태적 연결성 분석을 진행하였다. Chapter 2: 도시의 수목정보를 취득하는 것은 도시녹지 관리에 있어 필수적이다. LiDAR 기술의 발달로 도시수목의 3차원 정보를 취득할 수 있게 되었으며, 이를 통해 수목높이와 수목구조, 지상 바이오매스 등을 높은 정확도로 산출할 수 있게 되었다. 항공 LiDAR는 넓은 범위의 공간정보를 높은 정확도로 측정하는 특성을 지녀 산림 모니터링 분야에서 활발히 활용되고 있다. 하지만 항공 LiDAR 데이터의 취득은 항공기 운용비, 장비관련 막대한 비용이 발생하고 운용에 있어 전문성을 요구하며 대상의 점군밀도가 상대적으로 낮다는 단점을 지닌다. 반면 지상 LiDAR와 이동형 LiDAR는 운용하기 편리하고 높은 점군밀도를 출력한다는 점에서 항공 LiDAR의 단점을 극복할 수 있다. 이처럼 다양한 LiDAR 시스템의 등장과 이를 활용한 생태계 모니터링 연구 시도가 증가하면서 LiDAR 시스템간 효율적인 운용과 데이터의 보완 방법들이 요구되고 있다. 하지만 현재까지 ALS, TLS, MLS의 3개의 시스템을 통해 취득된 수목 정보를 서로 비교하고, 서로 대체가능한 수목정보를 도출한 연구는 많이 진행된 바 없다. 따라서 본 학위논문의 Chapter 2에서는 ALS, TLS, MLS 통해 취득된 도시의 수목정보를 서로 비교하여 일치성을 평가하고, 어떠한 수목구조관련 지표가 세 LiDAR 시스템 사이에서 대체가능한지 다루고 있다. 세부적으로 Chapter 2는 수목구조관련 지표가 단목차원과 군집차원, 수목구조에 따라 ALS, TLS, MLS 시스템에서 차이가 발생하는지에 대한 내용을 담고 있다. 천안시 도시공원 9개소에서 ALS 데이터는 2017년 5월 14일, TLS 데이터는 2017년 5월 10일과 11일, MLS 데이터는 2020년 4월 21에서 25일 취득되었다. 취득된 데이터셋은 수관의 겹침 여부에 따라 단목과 군집으로 분류되었으며, 3개의 페어(ALS-TLS, MLS-TLS, ALS-MLS)로 수관의 퍼센타일 높이, 수관복잡성, 면적 등의 수목구조관련 변수들을 1:1 비교하였다. 항공 LiDAR 데이터를 통해 도출된 수목구조관련 변수들을 참조로 하여 평균제곱근오차(RMSE), 편향(bias), 피어슨 상관계수(r) 등을 계산하고 세 LiDAR 시스템 사이의 일치성을 평가하였다. 평가 결과 ZMAX, CHM관련 수관높이 관련 변수들, 그리고 수관면적이 높은 일치성을 보였다(RMSE% 0.900). 특히 CHM을 통해 도출된 수관높이 관련 변수들은 단목과 군집에서 세개의 LiDAR 시스템간 통계적인 차이를 보이지 않았다(p > 0.05). 반면 퍼센타일 수관높이와 평균 수관높이 등은 매우 낮은 일치성을 나타냈으며, 세 페어에서 도출된 편향은 수고, 수관복잡성과 약한 선형관계를 나타냈다(r >, p < 0.05). Chapter 3: 수관동태는 숲의 건강성을 반영한다. 특히, 자연적·인위적인 교란에 의해 발생한 숲틈은 숲 내부에 빛의 투과율, 온도, 습도 등에 영향을 끼쳐 주변 환경의 변화를 야기한다. 따라서 숲틈을 탐지하고 모니터링하는 것은 숲의 동태를 이해하는데 있어 매우 중요하다. 항공 LiDAR 센서를 활용할 경우 위성영상이나 항공사진 등 2차원 데이터로 탐지하기 어려운 숲틈 또는 개방공간의 탐지와 수관의 3차원 형상의 취득이 가능하다. Chapter 3에서는 2012년도부터 2015년도 4개년의 항공 LiDAR를 활용하여 자연형 도시공원(봉서산)의 수관과 숲틈의 수평적 수직적 변화양상을 추정하였다. 수관은 높이 5m를 기준으로 상층부와 하층부 수관으로 분류되었으며, 수관높이모델(canopy height model, CHM)을 생성하여 연간변화를 탐지하였다. 연구결과 상층부 및 하층부 수관의 수직생장량과 엽면적지수는 일정한 연간 변화양상을 보인 반면, 수평적 변화와 엽면적밀도는 불규칙적인 연간 변화양상을 보였다. 전반적으로 상층부 수관은 높이 12m에서 측방향 생장을 하는 것으로 나타났으며, 하층부 수관 중 숲틈에서는 높이 5m에서 측방향 생장이 활발하게 나타났다. LiDAR 데이터의 연간 변화 탐지를 통해 자연적으로 형성된 숲틈의 경우 생장과 교란 측면에서 매우 활발한 동태가 발생하고 있으며, 인위적으로 형성된 개방공간의 경우 수관의 동태가 다소 침체됨을 도출하였다. Chapter 4는 도시 내 건물과 녹지의 3차원 구조를 입력자료로 활용하여 도시의 생태적 연결성을 평가하는 연구를 다룬다. 도시 내 생태적 연결성 도출과 관련한 연구는 도시와 녹지의 형태 등을 주요 변수로 하여 진행이 되고 있다. 그러나 3차원적인 특성인 도시 건물의 부피, 수목의 수직적인 구조 등을 고려한 연결성 분석은 많이 진행된 바 없다. 연구 대상지는 천안시 시청을 중심으로한 4 km × 4 km 지역으로, 2015년에 취득된 항공 LiDAR와 같은 해 취득된 조류 종 조사 데이터를 활용하여 1)도시 내 건물과 녹지의 3차원 구조와 조류 종 다양성 사이 관계를 살피고, 2)조류 종 다양성과 상관관계를 가지는 3차원 구조변수를 활용하여 전류흐름기반 매개중심성 연결성 분석(CFBC)을 진행하였다. 연구결과 건축물의 부피와 수목높이 8-10m의 녹지 부피비가 면적당 조류 종 풍부도와 스피어만 순위상관관계에서 높은 상관관계(ρ> 0.6)를 나타냈다. 연결성 분석의 결과는 입력변수의 공간차원(2D 및 3D)에 따라 다르게 나타났다. 특히 도시숲, 대로변, 아파트단지내 녹지 등에서 2D 기반 CFBC와 3D기반 CFBC는 통계적으로 유의미한 차이를 보였다. 또한 도시녹지의 3D 기반 CFBC의 경우 같은 녹지 면적임에도 수관의 구조적인 특성에 따라 높은 차이가 나타남을 확인하였다. 3D CFBC 분석결과를 통해 고층 건물 주변부, 고밀도 아파트단지, 고밀 시가화지역 등이 낮은 중심성을 보여 고립지역으로 나타났으며, 건물 사이 공지 내 식생은 연결성이 고립된 지역과 핵심지역을 연결하는 기능을 나타냈다. 이 학위논문은 서로 다른 LiDAR 시스템을 활용하여 단목, 경관 지역단위 등 다양한 공간 스케일에서의 도시경관구조 분석, 도시녹지구조와 토지이용 등에 따른 시간적 변화양상, 도시경관구조가 가지는 생태적 의미 등과 관련된 내용을 다루고 있다. 향후 Global Ecosystem Dynamics Investigation(GEDI) 미션의 데이터를 활용하여 본 학위논문에서 다루는 지역규모의 연구를 국가단위, 대륙단위 등으로 확장할 수 있을 것이며 이를 통해 도시생태계 구조와 그 기능 사의 관계를 이해하는데 도움을 줄 수 있을 것이다.Chapter 1. Introduction 1 1. Background 1 1.1. Urbanization and the importance of urban green spaces 1 1.2. Urban landscape and Light detection and ranging application 1 2. Purpose 6 Chapter 2. Comparing tree structures derived among multiple LiDAR systems in urban parks 10 1. Introduction 10 2. Methods and materials 12 2.1. Study site and tree classification 12 2.2. LiDAR survey and processing 14 2.3. Deriving the structural variables of the parks 17 2.4. Assessing the accuracy of the LiDAR-derived indices 18 3. Results 19 3.1. Comparing height metrics among the three LiDAR systems 19 3.2. Comparing CHM-derived canopy height metrics from each LiDAR systems 22 3.3. Comparing the area and the Rumple index determined using the LiDAR systems 23 4. Discussion 25 4.1. LiDAR configurations and data acquisition time intervals 25 4.2. Uncertainty of the structural indices derived from the three LiDAR systems 28 Chapter 3. Urban forest growth and gap dynamics detected by yearly repeated airborne LiDAR 31 1. Introduction 31 2. Methods and Materials 33 2.1. Field survey 33 2.2. Canopy opening detection 34 2.3. Airborne LiDAR dataset acquisition and registration 35 2.4. Generation of height models and change detection 36 2.5. Gap detection and classification 38 2.6. Estimating changes of vertical canopy distribution and canopy complexity 38 3. Results 39 3.1. Pixel and hexagon height model-based change detection 39 3.2. Continuous one-year vertical growth area 41 3.3. Open canopy change detection 42 3.4. Changes in vertical canopy structures in High Canopy and Open Canopy 43 4. Discussion 45 4.1. What are the differences between the canopy structural changes derived from annual change detections and three-year interval change detection? 45 4.2. What are the characteristics of the structural changes according to the different canopy classes (e.g., high canopies and low canopies) in the urban forest? 46 Chapter 4. LiDAR-derived three-dimensional ecological connectivity mapping 49 1. Introduction 49 2. Materials and Methods 51 2.1. Study area and avian species observation 52 2.2. Airborne LiDAR acquisition, preprocessing and classification and deriving structural variables 53 2.3. Correlation analysis and selection of structural variables 55 2.4. 2D and 3D ecological networks 55 3. Results 56 3.1. Avian species survey 56 3.2. Correlation analyses and variable selection 56 3.3. Connectivity analysis results 58 3.4. Correlation between connectivity results with bird species diversity 59 3.5. Differences between 2D- and 3D-based CFBCs 60 4. Discussion 61 4.1. Vegetation and building structures and bird species diversity 61 4.2. 3D-based connectivity results 62 4.3. Differences between 2D and 3D network analyses 64 4.3.1. Forest and artificial green area 65 4.3.2. Roads and residential areas 66 Appendix 67 Chapter 5. Conclusion 70 1. Combination with multiple LiDAR data for surveying structures of urban green spaces 70 2. Multi-temporal urban forest gap monitoring 71 3. Ecological connectivity analysis using LiDAR 71 4. LiDAR application to Urban ecosystem monitoring 72 5. Expanding spatiotemporal scale and further works 73 Acknowledgments 75 Reference 76 Abstract in Korean 85박

Estimating Free-Flow Speed with LiDAR and Overhead Imagery

Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow speed using LiDAR (Light Detection and Ranging) point clouds and satellite imagery. Employing deep learning for high-level pattern recognition and feature extraction, we present methods for predicting free-flow speed across the state of Kentucky

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Methodical basis for landscape structure analysis and monitoring: inclusion of ecotones and small landscape elements

Habitat variation is considered as an expression of biodiversity at landscape level in addition to genetic variation and species variation. Thus, effective methods for measuring habitat pattern at landscape level can be used to evaluate the status of biological conservation. However, the commonly used model (i.e. patch-corridor-matrix) for spatial pattern analysis has deficiencies. This model assumes discrete structures within the landscape without explicit consideration of “transitional zones” or “gradients” between patches. The transitional zones, often called “ecotones”, are dynamic and have a profound influence on adjacent ecosystems. Besides, this model takes landscape as a flat surface without consideration of the third spatial dimension (elevation). This will underestimate the patches’ size and perimeter as well as distances between patches especially in mountainous regions. Thus, the mosaic model needs to be adapted for more realistic and more precise representation of habitat pattern regarding to biodiversity assessment. Another part of information that has often been ignored is “small biotopes” inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. The present work originates from the integration of the third spatial dimension in land-cover classification and landscape structure analysis. From the aspect of data processing, an integrated approach of Object-Based Image Analysis (OBIA) and Pixel-Based Image Analysis (PBIA) is developed and applied on multi-source data set (RapidEye images and Lidar data). At first, a general OBIA procedure is developed according to spectral object features based on RapidEye images for producing land-cover maps. Then, based on the classified maps, pixel-based algorithms are designed for detection of the small biotopes and ecotones using a Normalized Digital Surface Model (NDSM) which is derived from Lidar data. For describing habitat pattern under three-dimensional condition, several 3D-metrics (measuring e.g. landscape diversity, fragmentation/connectivity, and contrast) are proposed with spatial consideration of the ecological functions of small biotopes and ecotones. The proposed methodology is applied in two real-world examples in Germany and China. The results are twofold. First, it shows that the integrated approach of object-based and pixel-based image processing is effective for land-cover classification on different spatial scales. The overall classification accuracies of the main land-cover maps are 92 % in the German test site and 87 % in the Chinese test site. The developed Red Edge Vegetation Index (REVI) which is calculated from RapidEye images has been proved more efficient than the traditionally used Normalized Differenced Vegetation Index (NDVI) for vegetation classification, especially for the extraction of the forest mask. Using NDSM data, the third dimension is helpful for the identification of small biotopes and height gradient on forest boundary. The pixel-based algorithm so-called “buffering and shrinking” is developed for the detection of tree rows and ecotones on forest/field boundary. As a result the accuracy of detecting small biotopes is 80 % and four different types of ecotones are detected in the test site. Second, applications of 3D-metrics in two varied test sites show the frequently-used landscape diversity indices (i.e. Shannon’s diversity (SHDI) and Simpson’s diversity (SIDI)) are not sufficient for describing the habitats diversity, as they quantify only the habitats composition without consideration on habitats spatial distribution. The modified 3D-version of Effective Mesh Size (MESH) that takes ecotones into account leads to a realistic quantification of habitat fragmentation. In addition, two elevation-based contrast indices (i.e. Area-Weighted Edge Contrast (AWEC) and Total Edge Contrast Index (TECI)) are used as supplement to fragmentation metrics. Both ecotones and small biotopes are incorporated into the contrast metrics to take into account their edge effect in habitat pattern. This can be considered as a further step after fragmentation analysis with additional consideration of the edge permeability in the landscape structure analysis. Furthermore, a vector-based algorithm called “multi-buffer” approach is suggested for analyzing ecological networks based on land-cover maps. It considers small biotopes as stepping stones to establish connections between patches. Then, corresponding metrics (e.g. Effective Connected Mesh Size (ECMS)) are proposed based on the ecological networks. The network analysis shows the response of habitat connectivity to different dispersal distances in a simple way. Those connections through stepping stones act as ecological indicators of the “health” of the system, indicating the interpatch communications among habitats. In summary, it can be stated that habitat diversity is an essential level of biodiversity and methods for quantifying habitat pattern need to be improved and adapted to meet the demands for landscape monitoring and biodiversity conservation. The approaches presented in this work serve as possible methodical solution for fine-scale landscape structure analysis and function as “stepping stones” for further methodical developments to gain more insights into the habitat pattern.Die Lebensraumvielfalt ist neben der genetischen Vielfalt und der Artenvielfalt eine wesentliche Ebene der Biodiversität. Da diese Ebenen miteinander verknüpft sind, können Methoden zur Messung der Muster von Lebensräumen auf Landschaftsebene erfolgreich angewandt werden, um den Zustand der Biodiversität zu bewerten. Das zur räumlichen Musteranalyse auf Landschaftsebene häufig verwendete Patch-Korridor-Matrix-Modell weist allerdings einige Defizite auf. Dieses Modell geht von diskreten Strukturen in der Landschaft aus, ohne explizite Berücksichtigung von „Übergangszonen“ oder „Gradienten“ zwischen den einzelnen Landschaftselementen („Patches“). Diese Übergangszonen, welche auch als „Ökotone“ bezeichnet werden, sind dynamisch und haben einen starken Einfluss auf benachbarte Ökosysteme. Außerdem wird die Landschaft in diesem Modell als ebene Fläche ohne Berücksichtigung der dritten räumlichen Dimension (Höhe) betrachtet. Das führt dazu, dass die Flächengrößen und Umfänge der Patches sowie Distanzen zwischen den Patches besonders in reliefreichen Regionen unterschätzt werden. Daher muss das Patch-Korridor-Matrix-Modell für eine realistische und präzise Darstellung der Lebensraummuster für die Bewertung der biologischen Vielfalt angepasst werden. Ein weiterer Teil der Informationen, die häufig in Untersuchungen ignoriert werden, sind „Kleinbiotope“ innerhalb größerer Patches (z. B. Feldhecken, Baumreihen, Feldgehölze oder Einzelbäume). Dadurch wird die Heterogenität innerhalb von Patches unterschätzt. Die vorliegende Arbeit basiert auf der Integration der dritten räumlichen Dimension in die Landbedeckungsklassifikation und die Landschaftsstrukturanalyse. Mit Methoden der räumlichen Datenverarbeitung wurde ein integrierter Ansatz von objektbasierter Bildanalyse (OBIA) und pixelbasierter Bildanalyse (PBIA) entwickelt und auf einen Datensatz aus verschiedenen Quellen (RapidEye-Satellitenbilder und Lidar-Daten) angewendet. Dazu wird zunächst ein OBIA-Verfahren für die Ableitung von Hauptlandbedeckungsklassen entsprechend spektraler Objekteigenschaften basierend auf RapidEye-Bilddaten angewandt. Anschließend wurde basierend auf den klassifizierten Karten, ein pixelbasierter Algorithmus für die Erkennung von kleinen Biotopen und Ökotonen mit Hilfe eines normalisierten digitalen Oberflächenmodells (NDSM), welches das aus LIDAR-Daten abgeleitet wurde, entwickelt. Zur Beschreibung der dreidimensionalen Charakteristika der Lebensraummuster unter der räumlichen Betrachtung der ökologischen Funktionen von kleinen Biotopen und Ökotonen, werden mehrere 3D-Maße (z. B. Maße zur landschaftlichen Vielfalt, zur Fragmentierung bzw. Konnektivität und zum Kontrast) vorgeschlagen. Die vorgeschlagene Methodik wird an zwei realen Beispielen in Deutschland und China angewandt. Die Ergebnisse zeigen zweierlei. Erstens zeigt es sich, dass der integrierte Ansatz der objektbasierten und pixelbasierten Bildverarbeitung effektiv für die Landbedeckungsklassifikation auf unterschiedlichen räumlichen Skalen ist. Die Klassifikationsgüte insgesamt für die Hauptlandbedeckungstypen beträgt 92 % im deutschen und 87 % im chinesischen Testgebiet. Der eigens entwickelte Red Edge-Vegetationsindex (REVI), der sich aus RapidEye-Bilddaten berechnen lässt, erwies sich für die Vegetationsklassifizierung als effizienter verglichen mit dem traditionell verwendeten Normalized Differenced Vegetation Index (NDVI), insbesondere für die Gewinnung der Waldmaske. Im Rahmen der Verwendung von NDSM-Daten erwies sich die dritte Dimension als hilfreich für die Identifizierung von kleinen Biotopen und dem Höhengradienten, beispielsweise an der Wald/Feld-Grenze. Für den Nachweis von Baumreihen und Ökotonen an der Wald/Feld-Grenze wurde der sogenannte pixelbasierte Algorithmus „Pufferung und Schrumpfung“ entwickelt. Im Ergebnis konnten kleine Biotope mit einer Genauigkeit von 80 % und vier verschiedene Ökotontypen im Testgebiet detektiert werden. Zweitens zeigen die Ergebnisse der Anwendung der 3D-Maße in den zwei unterschiedlichen Testgebieten, dass die häufig genutzten Landschaftsstrukturmaße Shannon-Diversität (SHDI) und Simpson-Diversität (SIDI) nicht ausreichend für die Beschreibung der Lebensraumvielfalt sind. Sie quantifizieren lediglich die Zusammensetzung der Lebensräume, ohne Berücksichtigung der räumlichen Verteilung und Anordnung. Eine modifizierte 3D-Version der Effektiven Maschenweite (MESH), welche die Ökotone integriert, führt zu einer realistischen Quantifizierung der Fragmentierung von Lebensräumen. Darüber hinaus wurden zwei höhenbasierte Kontrastindizes, der flächengewichtete Kantenkontrast (AWEC) und der Gesamt-Kantenkontrast Index (TECI), als Ergänzung der Fragmentierungsmaße entwickelt. Sowohl Ökotone als auch Kleinbiotope wurden in den Berechnungen der Kontrastmaße integriert, um deren Randeffekte im Lebensraummuster zu berücksichtigen. Damit kann als ein weiterer Schritt nach der Fragmentierungsanalyse die Randdurchlässigkeit zusätzlich in die Landschaftsstrukturanalyse einbezogen werden. Außerdem wird ein vektorbasierter Algorithmus namens „Multi-Puffer“-Ansatz für die Analyse von ökologischen Netzwerken auf Basis von Landbedeckungskarten vorgeschlagen. Er berücksichtigt Kleinbiotope als Trittsteine, um Verbindungen zwischen Patches herzustellen. Weiterhin werden entsprechende Maße, z. B. die Effective Connected Mesh Size (ECMS), für die Analyse der ökologischen Netzwerke vorgeschlagen. Diese zeigen die Auswirkungen unterschiedlicher angenommener Ausbreitungsdistanzen von Organismen bei der Ableitung von Biotopverbundnetzen in einfacher Weise. Diese Verbindungen zwischen Lebensräumen über Trittsteine hinweg dienen als ökologische Indikatoren für den „gesunden Zustand“ des Systems und zeigen die gegenseitigen Verbindungen zwischen den Lebensräumen. Zusammenfassend kann gesagt werden, dass die Vielfalt der Lebensräume eine wesentliche Ebene der Biodiversität ist. Die Methoden zur Quantifizierung der Lebensraummuster müssen verbessert und angepasst werden, um den Anforderungen an ein Landschaftsmonitoring und die Erhaltung der biologischen Vielfalt gerecht zu werden. Die in dieser Arbeit vorgestellten Ansätze dienen als mögliche methodische Lösung für eine feinteilige Landschaftsstrukturanalyse und fungieren als ein „Trittsteine” auf dem Weg zu weiteren methodischen Entwicklungen für einen tieferen Einblick in die Muster von Lebensräumen