Estimation of some stand parameters using digital aerial photographs for conservation and service oriented forests

Forest inventory, which is the first step of forest management planning, is the most difficult stage that requires much time and a lot of efforts. To reduce fieldworks that are considered time consuming and expensive methods of ground measurements, remote sensing data are widely used. Aerial photographs have been an integral part of forest inventory data in Turkey since 1963. Panchromatic and RGBI (Red, Green, Blue, Infrared) aerial photographs acquired by digital aerial cameras proved to be very important in forest inventory. They have maintained their importance for forest management planning process. The aim of this study is to construct a fast and practical inventory model that requires least fieldwork for forest management planning process. Pixel values and vegetation indices (NDVI, DVI, IPVI, RVI and PCA), obtained from remote sensing data, and stand parameters (stand volume, volume increment and number of trees) have been compared statistically. Black pine Pinus nigra J. F. Arnold plantations located in the south-east region of Turkey, Çelikhan Forest Planning Unit, was chosen as a research area. 0.5 meter spacing and 8 bit radiometric resolution Ultracam-X Digital Aerial Photos were used as remote sensing data. According to statistical analysis, IPVI and Green Band values provided the highest evaluation coefficient compared to the models developed for the estimation of stand parameters. Adjusted R square of stand volume, volume increment and the number of tree in the models were found to yield 0.74, 0.73 and 0.50 respectively. It was concluded that stand characteristics estimated by statistical models can be used for forest areas managed for conservation and service purposes

Remote sensing applications in forestry - The development of an earth resources information system using aerial photographs and digital computers Annual progress report

Earth resources information system using aerial photographs and digital computer

Trooppisten alkuperäismetsien monitorointi Taita Hillsin alueella digitaalisen ilmakuva-aineiston avulla

The loss and degradation of forest cover is currently a globally recognised problem. The fragmentation of forests is further affecting the biodiversity and well-being of the ecosystems also in Kenya. This study focuses on two indigenous tropical montane forests in the Taita Hills in southeastern Kenya. The study is a part of the TAITA-project within the Department of Geography in the University of Helsinki. The study forests, Ngangao and Chawia, are studied by remote sensing and GIS methods. The main data includes black and white aerial photography from 1955 and true colour digital camera data from 2004. This data is used to produce aerial mosaics from the study areas. The land cover of these study areas is studied by visual interpretation, pixel-based supervised classification and object-oriented supervised classification. The change of the forest cover is studied with GIS methods using the visual interpretations from 1955 and 2004. Furthermore, the present state of the study forests is assessed with leaf area index and canopy closure parameters retrieved from hemispherical photographs as well as with additional, previously collected forest health monitoring data. The canopy parameters are also compared with textural parameters from digital aerial mosaics. This study concludes that the classification of forest areas by using true colour data is not an easy task although the digital aerial mosaics are proved to be very accurate. The best classifications are still achieved with visual interpretation methods as the accuracies of the pixel-based and object-oriented supervised classification methods are not satisfying. According to the change detection of the land cover in the study areas, the area of indigenous woodland in both forests has decreased in 1955-2004. However in Ngangao, the overall woodland area has grown mainly because of plantations of exotic species. In general, the land cover of both study areas is more fragmented in 2004 than in 1955. Although the forest area has decreased, forests seem to have a more optimistic future than before. This is due to the increasing appreciation of the forest areas.Metsien väheneminen ja niiden laadun heikkeneminen on maailmanlaajuisesti tunnustettu ongelma. Metsien pirstoutuminen vaikuttaa biodiversiteettiin ja ekosysteemien hyvinvointiin myös Keniassa. Tämä tutkimus keskittyy kahden trooppisen alkuperäisvuoristometsän tutkimiseen Taita Hillsin alueella Kaakkois-Keniassa. Tutkimus on osa Helsingin yliopiston maantieteen laitoksen TAITA-projektia. Tutkimusmetsiä, Ngangaoa ja Chawiaa tutkitaan kaukokartoitus- ja paikkatietomenetelmien avulla. Tutkimuksen pääaineiston muodostavat mustavalkoiset ilmakuvat vuodelta 1955 ja digitaaliset oikeaväri-ilmakuvat vuodelta 2004. Näistä ilmakuvista muodostetaan ilmakuvamosaiikit tutkimusalueilta. Alueiden maanpeite luokitellaan kolmella metodilla: visuaalisella tulkinnalla, pikselipohjaisella ohjatulla luokituksella sekä objekti-orientoidulla ohjatulla luokituksella. Metsäpinta-alan muutosta vuosina 1955-2004 tutkitaan visuaalisten luokitusten perusteella käyttämällä paikkatietomenetelmiä. Tutkimusmetsien kuntoa arvioidaan lehtipinta-alaindeksin ja latvuksen sulkeituneisuuden avulla. Nämä parametrit saadaan käyttämällä hemisfäärisiä valokuvia. Lisäksi tutkimuksessa käytetään metsien kuntoa arvioivaa aiemmin kerättyä tutkimustietoa. Latvusparametreja verrataan digitaali-ilmakuvamosaiikeilta saatuihin tekstuurisiin parametreihin. Yhteenvetona voidaan sanoa, että metsäalueiden luokitus oikeaväri-ilmakuvia käyttämällä ei ole helppoa, vaikka itse digitaali-ilmakuvista tehdyt mosaiikit olisivat erittäin tarkkoja. Parhaat luokitustulokset saavutetaan edelleen visuaalisella tulkinnalla, sillä pikselipohjainen ja objekti-orientoitu ohjattu luokitus eivät saavuta tarpeeksi hyvää luotettavuutta. Tutkimusalueiden maanpeitteen muutostulkinnan mukaan alkuperäismetsän osuus on vähentynyt sekä Ngangaossa että Chawiassa 1955-2004. Ngangaossa metsän kokonaisala on kuitenkin lisääntynyt lähinnä eksoottisten puulajien istutusten vuoksi. Molempien tutkimusalueiden maanpeite on huomattavasti pirstoutuneempaa vuonna 2004 kuin vuonna 1955. Vaikka metsäala on pienentynyt, tutkimusmetsien tulevaisuus näyttää paremmalta kuin aiemmin. Tämä johtuu lähinnä kasvavasta metsien arvostuksesta

Evaluation of Skylab (EREP) data for forest and rangeland surveys

The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent

Fire models and methods to map fuel types: The role of remote sensing.

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information andmethods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques

The Detection of Forest Structures in the Monongahela National Forest Using LiDAR

The mapping of structural elements of a forest is important for forestry management to provide a baseline for old and new-growth trees while providing height strata for a stand. These activities are important for the overall monitoring process which aids in the understanding of anthropogenic and natural disturbances. Height information recorded for each discrete point is key for the creation of canopy height, canopy surface, and canopy cover models. The aim of this study is to assess if LiDAR can be used to determine forest structures. Small footprint, leaf-off LiDAR data were obtained for the Monongahela National Forest, West Virginia. This dataset was compared to Landsat imagery acquired for the same area. Each dataset endured supervised classifications and object oriented segmentation with random forest classifications. These approaches took into account derived variables such as, percentages of canopy height, canopy cover, stem density, and normalized difference vegetation index, which were converted from the original datasets. Evaluation of the study depicted that the classification of the Landsat data produced results ranging between 31.3 and 50.2%, whilst the LiDAR dataset produced accuracies ranging from 54.7 to 80.1%. The results of this study increase the potential of LiDAR to be used regularly as a forestry management technique and warrant future research

An Overview of Remote Sensing in Russian Forestry

The Russian Federation possesses vast forested areas, containing about 23% of the world's closed forests. A significant part of these forestlands is neither managed nor regularly monitored. This is due in part to the absence of developed infrastructure in the remote northern regions, which hampers the collection of data on forest inventory and monitoring in all areas by precise and expensive on-ground methods. As a result, the monitoring in all areas by precise and expensive on-ground methods. As a result, the former Soviet Union conducted intensive research on remote sensing during the last few decades, resulting in significant achievements. However, there has been a noticeable decline in remote sensing research and applications in the Russian forest sector from 1990-1998. Russia needs a new system of forest inventory and monitoring capable of providing reliable, practical information for sustainable forest management. Such a system should take into account current national demands on the Russian forest sector as well as the international obligations of the country. Remote sensing methods are an indispensable part of such a system. These methods will play a crucial role in critical applications such as ensuring the sustainability of forest management, protecting threatened forests, fulfilling the countrys Kyoto Protocol obligations, and others. This paper presents an overview of past and current remote sensing methods in the Russian forest sector, including both practical and scientific applications. Based on this overview, relevant applications of remote sensing methods in the Russian forest sector are discussed. This discussion considers current Russian economic conditions and the direction of political and social development of the country

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report

An integrated study of earth resources in the state of California using remote sensing techniques

The University of California has been conducting an investigation which seeks to determine the usefulness of modern remote sensing techniques for studying various components of California's earth resources complex. Most of the work has concentrated on California's water resources, but with some attention being given to other earth resources as well and to the interplay between them and California's water resources

Taxonomic identification of Amazonian tree crowns from aerial photography

Question: To what extent can aerial photography be used for taxonomic identification of Amazonian tree crowns? Objective: To investigate whether a combination of dichotomous keys and a web-based interface is a suitable approach to identify tree crowns. Location: The fieldwork was conducted at Tiputini Biodiversity Station located in the Amazon, eastern Ecuador. Methods: High-resolution imagery was taken from an airplane flying at a low altitude (600 m) above the ground. Imagery of the observable upper layer of the tree crowns was used for the analysis. Dichotomous identification keys for different types of crowns were produced and tested. The identification keys were designed to be web-based interactive, using Google Earth as the main online platform. The taxa analysed were Iriartea, Astrocaryum, Inga, Parkia, Cecropia, Pourouma, Guarea, Otoba, Lauraceae and Pouteria. Results: This paper demonstrates that a combination of photo-imagery, dichotomous keys and a web-based interface can be useful for the taxonomic identification of Amazonian trees based on their crown characteristics. The keys tested with an overall identification accuracy of over 50% for five of the ten taxa with three of them showing accuracy greater than 70% (Iriartea, Astrocaryum and Cecropia). Conclusions: The application of dichotomous keys and a web-based interface provides a new methodological approach for taxonomic identification of various Amazonian tree crowns. Overall, the study showed that crowns with a medium-rough texture are less reliably identified than crowns with smoother or well-defined surfaces