Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest

Advances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%).Xunta de GaliciaAgencia Estatal de Investigación | Ref. PID2019-111581RB-I00Universidade de Vig

KLASIFIKACIJA VRSTA DRVEĆA U PRIRODNOJ URBANOJ ŠUMI KORISTEĆI WORLDVIEW-2 SATELITSKE SNIMKE I LIDAR

A detailed tree species inventory is needed to sustainably manage a natural, mixed, heterogeneous urban forest. An object-based image analysis of a combination of high-resolution WorldView-2 multi-spectral satellite imagery and airborne laser scanning (LiDAR) data was tested for classification of individual tree crowns of five different tree species. The model training data were obtained from a systematic grid of plots in the forest. In total, 304 coniferous (Norway spruce and Scots pine) and 270 deciduous (European beech, Sessile and Pedunculate oak (combined), and Sweet chestnut) trees were identified in the field. The classification was performed by applying the support vector machine model. An accuracy assessment was performed by calculating a confusion matrix to evaluate the accuracy of the classification output by comparing the classification result to the independent test data. The overall accuracy of the classification was 58 %.Osnovni zadatak gospodarenja šumama je provedba inventure drveća. Posebno se to odnosi na blisko prirodi gospodarene urbane šume. Cilj ovog istraživanja je provjeriti može li se metoda analize snimaka (tzv. object-based image analysis – OBIA) kombinacijom WorldView-2 multispektralnih satelitskih snimaka visoke prostorne rezulocije i laserskog skeniranja (LiDAR-a) koristiti za uspješnu klasifikaciju krošanja pojedinačnih stabala različitih vrsta drveća u prirodnim, mješovitim i heterogenim urbanim šumama u Ljubljani (Slika 1).Terenska klasifikacija vrsta drveća provedena je postavljanjem mreže kružnih ploha (100x100 m) veličine od 2000 m2. Na svakoj od 332 plohe, registrirana su stabla iz dominantnog i kodominantnog sloja drveća. Ukupno je za analizu izdvojeno 574 stabala, od čega 304 stabla četinjača (obična smreka, obični bor) i 270 stabala listača (obična bukva, hrast lužnjak i kitnjak, pitomi kesten). Polovica uzorkovanih stabala tj. njihovih krošanja korišteno je kao probni set podataka u nadgledanoj klasifikaciji, dok je druga polovica uzorkovanih stabala korištena za ocjenu točnosti provedene klasifikacije (tzv. testni podaci).Za klasifikaciju su korištene WorldView-2 multispektralne satelitske snimke (8-kanalne), tzv. ‘Red-Edge’ normalizirani razlikovni vegetacijski indeks (NDVI) izračunat pomoću rubnog crvenog i crvenog spektralnog kanala te digitalni model krošanja (tzv. Digital Canopy Model – DCM) dobiven iz LiDAR podataka. Prostorna rezolucija WorldView-2 satelitskih snimaka iznosila je 1 m.Klasifikacija je provedena pomoću Exelis ENVI 5 kompjuterskog programa, primjenjujući tzv. pomoćni vektorski model. Preciznost procjene izračunata je na temelju izračunate matrice pogreške, uspoređujući rezultate klasifikacije s testnim podacima. Također je provedena analiza glavnih komponenata, koja je pokazala da je najveća varijabilnost (oko 85 %) objašnjena pomoću rubnog crvenog spektralnog kanala (705–745 nm), bližeg infracrvenog kanala – 1 (770–895 nm) te bližeg infracrvenog spektralnog kanala – 2 (860–1040 nm) WorldView-2 snimaka.Metoda analize snimaka (OBIA) kombinacijom WorldView-2 satelitskih snimaka I LiDAR podataka korištena u ovom istraživanju pokazala je obećavajuće rezultate pri klasifikaciji vrsta drveća u gustim, mješovitim i heterogenim prirodnim urbanim šumama, u kojima često dolazi do isprepletanja krošanja. Najpouzdaniji dobiveni rezultati odnose se na razlikovanje četinjača i listača. Kod sastojina s gustim krošnjama, posebice kod listača kod kojih je teško napraviti delineaciju krošanja, otežana je i manualna i automatska delineacija (segmentacija) krošanja. Ovo istraživanje novi je dokaz kako se primjenom podataka dobivenih metodama daljinskih istraživanja pruža mogućnost uštede u vremenu pri inventarizaciji vrsta drveća.Ukupna preciznost identifikacije iznosila je 58 %, a Kappa koeficijent je iznosio 0.421 (Tablica 4). Za svaku vrstu drveća izračunata je preciznost na osnovi razlike između preciznosti koju navodi proizvođač (postotak točno identificiranih piksela u odnosu na ukupan broj piksela na probnim podacima) i preciznosti korisnika. Rezultati tako dobivene preciznosti iznosili su 80 % za smreku, 70 % za hrastove lužnjak i kitnjak, 50 % za obični bor, 38 % za bukvu, te manje od 1 % za pitomi kesten

Interpretation of Forest Resources at the Individual Tree Level at Purple Mountain, Nanjing City, China, Using WorldView-2 Imagery by Combining GPS, RS and GIS Technologies

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely 10 cm, 15 cm, and 20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 x 3 pixels, 5 x 5 pixels and 7 x 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 +/- 15.14% averaged over the 90 plots. The numbers of the trees that were 15 cm and 20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 +/- 17.21% and 71.92 +/- 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the mixed pixels problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from -1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.ArticleREMOTE SENSING. 6(1):87-110 (2014)journal articl

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

The use of WorldView-2 satellite data in urban tree species mapping by object-based image analysis technique

The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling their growth and decline. This study focused on the detection of urban tree species by considering three types of tree species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of maximum likelihood classification and support vector machines. These methods were then compared with object-based image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy

Mapping eucalyptus species using worldview 3 and random forest

Recent advances in remote sensing technologies have allowed the development of new innovative methodologies to obtain geospatial information about Eucalyptus genus distribution. This is an important task for forest stakeholders due to the high presence of this genus in forest plantations worldwide. Therefore, the next step in research should focus on exploring remote sensing possibilities to discern between Eucalytpus species. It would be an important step forward in forest management since different Eucalyptus species present different characteristics and properties that imply different management plans and industrial usages. This study accomplish the classification of E. nitens and E. globulus, the most common Eucalyptus species in the Iberian Peninsula. Worldview-3 images and random forest are used in a forest area placed in Galicia (Northwest of Spain). The differentiation of Eucalyptus species resulted in a producer’s accuracy of 84% and a users’ accuracy of 70% for E. nitens, while for E. globulus accuracy metrics did not reach 70%. The most important bands in the classification were the coastal blue and the blue, followed by the red related ones. The resulting unequal accuracy metrics might be caused by an imbalanced presence of both species in the selected study area. Therefore, further studies might be developed in different locations