Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj \u3e 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images

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

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images

Assessing the utility of remotely sensed data and integrated topographic characteristics for determining tree stand structural complexity in a re-forested urban landscape.

Master of Science in Environmental Science. University of KwaZulu-Natal 2017.Transformation of natural landscapes into impervious built-up surfaces through urbanisation is known to significantly interfere with urban ecological integrity and its ability to provide environmental goods and services as well as accelerate climate change and associated impacts. Urban reforestation is widely promulgated as an ideal mitigation practice against impacts associated with urbanisation, however reforestation often has to compete with multiple and more “lucrative” urban land uses. This necessitates the optimisation of ecological benefits derived from reforestation within the limited available land. Such optimisation demands spatially explicit monitoring and evaluation (M&E). The recent proliferation of tree stand structural complexity (SSC) – a multidimensional index of the ecological performance of tree stands - offers great potential as an alternative indicator of ecological performance, instead of the one-dimensional traditional indicators such as Leaf Area Index, stem diameter and tree height. Furthermore, the recent advancements in remote sensing (RS) technology offers an improved potential of determining ecological performance across an urban reforested landscape. However, remotely sensed data costs and reliability often hinder their operational adoption. Consequently, the recent advancements in the freely available Sentinel 2 (S-2) data offer great potential for a cost effective operational M&E of SSC. The aim of this study was to i) Examine the utility of the freely available S-2 multispectral instrument imagery to determine SSC using the Partial Least Squares (PLS) regression technique within a re-forested urban landscape ii) Explore the potential of integrating topographic datasets with the S-2 data to determine SSC and iii) To rank the value of these variables in determining SSC. Tree structural data from a re-forested urban area was collected and a SSC index used to determine the area’s ecological performance. Multiple vegetation indices (VIs) were derived from the S-2 imagery while topographic variables (i.e. Topographic Wetness Index (TWI), slope, Area Solar Radiation (ASR), and elevation) were derived from a Digital Elevation Model (DEM). Results showed that the PLS model (n = 90) using the most important S-2 VIs (S2 REP, REIP, IRECI, GNDVI) produced a moderate predictive accuracy (0.215 NRMSECV) while topographybased model produced a high prediction accuracy (0.147 NRMSECV). Integrating the S-2 data with topographic information produced the highest prediction accuracy (0.13 NRMSECV). Furthermore, results indicate that SSC significantly varied across all topographic variables, with TWI and slope as the most important determinants of SSC. These results provide valuable spatially explicit information about the ecological performance of the reforested urban areas. Additionally, the study demonstrates the value of topographic data as an alternative predictor of SSC as well as the value of integrating the S-2 data with topographic characteristics in determining the performance of reforested areas

Forest resource modelling combining satellite imagery and LiDAR data

El sector forestal tiene un papel relevante en la transición hacia una economía innovadora y eficiente en el consumo de sus recursos. Conocer la disponibilidad espacial de los recursos forestales y su evolución temporal es crítico en la gestión forestal, tanto de los recursos maderables como de los no maderables. El uso de información procedente de sensores remotos se está convirtiendo en una opción cada vez más rigurosa y asequible para el desarrollo de esta tarea. Así, el conocimiento que estas herramientas proporcionan sobre el estado de desarrollo de las masas forestales y la disponibilidad de sus recursos permite hacer frente a los diferentes escenarios futuros que plantea el actual contexto de cambio global. Esta tesis caracteriza y evalúa diferentes recursos forestales mediante la combinación de información continua procedente de imágenes de satélite y datos LiDAR, con diferentes niveles de resolución espacial y espectral. Estos datos, apoyados en trabajo de campo, han sido calibrados y validados, demostrando un gran potencial. Discriminar diferentes especies y tipos de masa, tanto a nivel de árbol individual como de objeto, son objetivos alcanzables mediante el uso adecuado de estas herramientas, disminuyendo la dependencia histórica del trabajo de campo e integrando el cambio de escala en los inventarios tradicionales. Esta tesis desarrolla herramientas robustas capaces de evaluar recursos forestales a gran escala mediante modelos mixtos lineales y técnicas de modelización basadas en aprendizaje automático.Forestry sector plays an important role in the transition towards a new economy, driven by efficient resource consumption. Understanding the spatial distribution of forest resources and its temporal evolution is critical in forest management, both for timber and non-timber resources. Remote sensing information is becoming an increasingly precise and affordable option for the accomplishment of this task. The knowledge provided by these tools regarding stand development and availability of resources enables predicting future global change scenarios. This Doctoral Thesis assesses different forest resources combining continuous information derived from satellite images and LiDAR data at different spatial and spectral resolution levels. This information, supported by field work, has been calibrated and validated, showing a great potential. Species and stand types discrimination, both at individual tree and object levels, can be accomplished with these tools, decreasing the historical dependence of field work and integrating the scale change in traditional inventories. This PhD work aims to develop robust tools able to evaluate large-scale forest resources, by means of linear mixed models and machine learning.Departamento de Producción Vegetal y Recursos ForestalesDoctorado en Conservación y Uso Sostenible de Sistemas Forestale

Improved quantification of forest range shifts and their implications to ecosystem function in high-elevation forests

Rapid environmental changes are driving shifts in forest distribution across the globe with significant implications for ecosystem function and biodiversity. Despite the prevalence of forest range shifts across the globe, reliable estimations of changes in forest extent and structure at the elevational treeline (the elevational limit of forest distribution) are difficult to obtain due to limited access to mountainous environments. Remote sensing data is well suited to quantifying environmental change across large areas; however, a lack of published research that uses remotely sensed data in studies of mountain forests has led to uncertainty surrounding how much information about forest structure at the mountain treeline can be resolved in remotely sensed data. This uncertainty presents a major obstacle to landscape-scale quantification of forest range shifts and estimation of the impacts forest advance will have on ecosystem function and biodiversity in mountain systems. The distribution of high-elevation coniferous forests in the Central Mountain Range, Taiwan, has changed rapidly with increases in treeline elevation and forest density reported. Climate is considered to be the primary regulatory factor of the treeline in the Central Mountain Range. However, topography modifies the response of treeline advance to environmental change resulting in a structurally diverse treeline. This research combines a network of field observations across the Central Mountain Range, Taiwan, with aerial photography and multispectral satellite imagery to 1) determine which spectral features derived from multispectral satellite remote sensing best explain variation in mountain treeline structure and the effect of sensor spatial resolution on the characterisation of structural variation; 2) quantify variation in rates of forest advance; 3) quantify the accuracy of forest change assessments using a sample-based area estimation and classifying spectral trends identified in a time-series of satellite remote sensing data, and 4) quantify changes in above-ground woody biomass. The results presented here show that the green, red and short-wave infrared spectral bands and vegetation indices derived from these spectral bands offer the best characterisation of vegetation structure across the treeline ecotone with R2 values reported up to 0.723. Sample-based change assessment using repeat aerial photography shows a 295.0 ha increase in forest area and a 115.1 m increase in the mean elevation of forest establishment between 1963 and 2016. The rate of forest advance is spatially variable with forest establishment occurring most rapidly on east and south facing slopes with gradients of 0-20° and is also temporally variable with the rate of forest establishment peaking between 1980 and 2001. The classification of spectral trends in time-series analysis shows that Landsat-based change estimates underestimate the area of forest advance in the Central Mountain Range. However, the general pattern and direction of habitat change are consistent with those derived from sample-based estimates of change using repeat aerial photography offering the opportunity for error adjustment. Consequently, the results presented within this thesis show a net gain in above-ground woody biomass of 4688.7 t C in areas above 2400 m a.s.l. in the Central Mountain Range, Taiwan, and a reduction in the area of alpine grassland. The methods presented in this thesis provide a major opportunity to improve the quantification of forest range shifts across mountain systems allowing the estimation of landscape-scale impacts of forest advance on biodiversity and ecosystem function in data-poor mountain regions

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0