Landsat TM Derived Forest Covertypes for Modelling Net Primary Production

Franklin, Steven E.; Lavigne, M. B.; Deuling, M. J.; Wulder, M.; Hunt, E. Raymond

doi:10.1080/07038992.1997.10855206

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…resulting in fe wer pixels requiring assessment to demonstrate local spatial autocorrelation. • iii 25 t----1----1----11-1 I forest polygon data supports the findings of Franklin et al ( 1997). Polygon information is normally intended for forest management activities, while the information present at the Landsat TM spatial resolution is often not of sufficient detail lor management purposes.…”

Section: Resultssupporting

confidence: 59%

“…The comparison also demonstrates how the G i * values are of variable or object-resolution. The extent of the spatial dependence objects generated indicate the utility of raster-based approaches when using remotely-sensed data for forest planning purposes (Holmgren and Thuresson, 1997) or for the generation of model inputs (Franklin et al, 1997). Visualization of the G;* results illustrates that the results convey meaningful spatial information, demonstrating the potential of the Getis statistic in a forestry remote sensing context.…”

Section: Resultsmentioning

confidence: 96%

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Local Spatial Autocorrelation Characteristics of Landsat TM Imagery of a Managed Forest Area

Wulder

Boots

2001

Canadian Journal of Remote Sensing

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RESUMEEn teledetection, les paysages en continu sont echantillonnes en fonction d'une grille de pixels d'egale dimension et d'espacement regulier. Une consequence de cette regularisation de surface est que les valeurs de pixels exhibent une autocorrelation spatiale positive. Ainsi, on peut considerer que l'etendue et la nature de l'autocorrelation spatiale constituent une caracteristique des donnees de teledetection utilisable en tant que source d'information. Toutefois, les mesures globales d'autocorrelation spatiale existantes fournissent peu de connaissances quant acette caracteristique etant donne qu'elles resument en une seule mesure toutes les interrelations spatiales. Par contraste, la mesure des indicateurs locaux d'association spatiale (LISA, local indicators of spatial indicators) permet d'evaluer pour chaque pixel de l'image ala fois Ie degre de dependance spatiale du pixel par rapport aux pixels voisins et l'amplitude des valeurs de variation dans le voisinage du pixel. Dans cette etude, on applique une mesure LISA, la mesure Getis (G; *), a des images Landsat TM d'une region forestiere amenagee. On note des relations entre, d'une part, l'autocorrelation spatiale au plan local telle que mesuree par (G; *) et, d'autre part, differentes bandes Landsat TM et differents types de couvert. De plus, l'information sur la dependance spatiale est presentee dans le contexte des polygones d'inventaire forestier indiquant la presence d'heterogeneite ou d'homogeneite spectrale a l'interieur des polygones forestiers. Cette recherche exploratoire confirme que l'irformation sur la dependance spatiale telle que derivee de la mesure de (G;*) constitue une nouvelle source importante d'irformation spatiale pour l'evaluation des images numeriques en milieu forestier SUMMARYIn remote sensing, continuous landscapes are sampled into a grid ofequally sized and regularly spaced pixels. One consequence of this surface regularization is that pixel values exhibit positive spatial autocorrelation. Accordingly, the extent and nature of spatial autocorrelation can be considered a characteristic of remotely-sensed data which may be exploited as an information source. However, existing global measures ofspatial autocorrelation provide little insight into this characteristic since they summarize all spatial interrelationships in a single measure. In contrast, local indicators of spatial association (LISA) measures assess for each pixel in the image both the degree of spatial dependence with neighbouring pixels and the magnitude ofvariate values in the neighbourhood ofthe pixel. In this study, one such LISA statistic, the Getis statistic (G;*), is applied to Landsat TM imagery of a managed forest region. Relationships are found between local spatial autocorrelation as measured by G;* and different Landsat TM image bands and differing cover types. Further, the spatial dependence information is presented in the context of forest inventory polygons indicating the presence of spectral heterogeneity or homogeneity within forest polygon areas....

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Section: Resultssupporting

confidence: 59%

Section: Resultsmentioning

confidence: 96%

Local Spatial Autocorrelation Characteristics of Landsat TM Imagery of a Managed Forest Area

Wulder

Boots

2001

Canadian Journal of Remote Sensing

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“…Process models are based on current knowledge of major ecological/biophysical processes, but suffer from a high level of complexity, large computational demand and the difficulty to calibrate. The integration of light‐use efficiency and process model algorithms is a potentially effective approach to estimate global and regional NPP using remote sensing data and ecological/biophysical processes (Bartelink et al 1997; Franklin et al 1997; Goetz and Prince 1998). In this paper, NPP was defined as: Where GPP is gross primary production, R mo is maintenance respiration by all other living parts except leaves and fine roots, and R g is growth respiration; Where ɛ g is light‐use efficiency, FPAR is the fraction of photosynthetically active radiation absorbed by green vegetation and PAR is the photosynthetically active radiation absorbed by green vegetation, and f1 ( T ) and f2 (β) are the effects of air temperature and soil moisture on photosynthesis, respectively (Christopher et al 1995; Sun 1996; Sun and Zhu 2001; David et al 2002; Zhao et al 2005).…”

Section: Methodsmentioning

confidence: 99%

Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

Peng¹,

Huang²,

ChengXia³

et al. 2008

JIPB

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Net primary productivity (NPP) is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climate-vegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) data and geographic information system (GIS) techniques. The impact of climatic factors (air temperature, precipitation and sunshine percentage) on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0 lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.

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“…A large amount of forest structure and inventory information can be extracted from such imagery (Hay and Niemann 1994, Lark 1996, St.-Onge and Cavayas 1995, Wulder et al 1998. In identifying different forest stands and characteristics of forest canopies (Franklin et al 2000), the resulting forest class stratification would be more complex than has been provided by earlier generation low-and medium-spatial resolution satellite imagery, such as the SPOT HRV and Landsat TM sensors (Congalton et al 1993, Bauer et al 1994, Wolter et al 1995, Franklin et al 1997. One common strategy has been to use the mean spectral response in an automated classification of forest inventory conditions, similar to the procedures used in low-and medium-spatial resolution classification methods.…”

Section: Introductionmentioning

confidence: 98%

Multi‐layer Forest Stand Discrimination with Spatial Co‐occurrence Texture Analysis of High Spatial Detail Airborne Imagery

Moskal

Franklin

2002

Geocarto International

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Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi-layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18 th , 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co-occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co-occurrence texture and spectral signatures combined, and a thirteen-class multi-layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per-plot classification accuracies by 15.7%, compared to the use of the spectral data alone.

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Landsat TM Derived Forest Covertypes for Modelling Net Primary Production

Cited by 14 publications

References 18 publications

Local Spatial Autocorrelation Characteristics of Landsat TM Imagery of a Managed Forest Area

Local Spatial Autocorrelation Characteristics of Landsat TM Imagery of a Managed Forest Area

Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

Multi‐layer Forest Stand Discrimination with Spatial Co‐occurrence Texture Analysis of High Spatial Detail Airborne Imagery

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