Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method

Franco-Lopez, Hector; Ek, Alan R.; Bauer, Marvin E.

doi:10.1016/s0034-4257(01)00209-7

Cited by 402 publications

(266 citation statements)

References 21 publications

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“…The performance of each k-NN configuration was evaluated using leave-one-out (LOO) cross-validation. Optimal k-NN values were determined using the highest Pearson correlation index (R) and the lowest root mean square error (RMSE) principle for pixel-wise accuracy (Franco-Lopez et al, 2001;Chirici et al, 2008;Dusseux et al, 2015). In the final step, we used the outperforming configuration for k-NN for estimating forest AGB over the study area.…”

Section: The Biome-bgc Modelmentioning

confidence: 99%

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Tian

Chen

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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A B S T R A C TIn this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China.Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R 2 = 0.70 and RMSE = 24.52 tones ha −1 ). We then obtained one seasonal cycle ( ) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (BiomeBioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R 2 = 0.75 and RMSE = 1. 27 gC m −2 d −1 for GPP, and R 2 = 0.61 and RMSE = 1.17 gC m −2 d −1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R 2 = 0.73 and RMSE = 46.65 g m −2 year −1 ). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.

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Section: The Biome-bgc Modelmentioning

confidence: 99%

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Tian

Chen

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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“…Buradaki K değeri kullanıcı tarafından öncelikle belirlenmesi gereken bir değerdir. Verilerin benzerliği Öklit, Manhattan ve Chebyshev uzaklık ölçütleri ile hesaplanır [20].…”

Section: K-nn (K-en Yakın Komşuluk K-nearest Neighbour)unclassified

Serviks Kanserinin Erken Teşhisi için Çok Katmanlı Sitoloji Küplerinde Çekirdek ve Sitoplazma Bölütlenmesi

Bilgin

Melek

2017

SDÜ Fen Bil Enst Der

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Özet: Dünya genelinde kadınlarda yaygın olarak görülen ve kanser ölümlerinin önde gelen nedenlerinden biri olan serviks kanseri, Pap smear testi sonucunda elde edilen görüntülerdeki hücre sayısı, şekli ve özelliklerinden yararlanılarak teşhis edilir. Pap smear testinin düzenli olarak yapılması ile serviks kanserinin erken teşhisi ya da servikste henüz kansere dönüşmemiş değişikliklerin saptanması mümkündür. Bu nedenle, servikal hücrelerin otomatik olarak bölütlenmesi ile test sonuçlarının hızlı ve doğru bir şekilde değerlendirilmesi oldukça önemlidir. Bu amaçla, Uluslararası Biyomedikal Görüntüleme Sempozyumu (ISBI'2015) kapsamında gerçekleştirilen, "2. Örtüşen Servikal Sitoloji Görüntü Bölütlenmesi Yarışması" tarafından sağlanan gerçek servikal hücre görüntüleri kullanılmıştır. Kullanılan görüntülerden elde edilen farklı veri kümeleri üzerinde kümeleme ve sınıflandırma yöntemleri ile bölütleme işlemi gerçekleştirilerek hücre çekirdeği, hücre sitoplazması ve görüntü arka planı net bir şekilde tespit edilmeye çalışılmıştır. Çalışmanın başarısı farklı servikal sitoloji görüntüleri üzerinde test edilmiştir. Sonuçlar, oluşturulan veri kümelerinin yapılarına ve kullanılan yöntemlere göre incelenmiştir. Bu çalışmanın serviks kanseri ile alakalı ilerideki çalışmalarda bir temel oluşturması temenni edilmektedir. ISBI'2015) are used. Cell nucleus, cell cytoplasm and the background of image have been tried to be determined clearly by performing clustering and classification methods on the different data sets obtained from the used images. The success of the study was tested on different cervical cytology images. The results were examined according to the formation of the data sets and the methods used. It is expected that this study may provide a basis for further studies related to cervical cancer. Nucleus and Cytoplasm Segmentation in Multilayered Cytology

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“…The leave-one-out (LOO) technique is a special case of bootstrapping in which a prediction is iteratively calculated for each individual element of the reference set using the remaining reference set observations [Lachenbruch and Mickey, 1986]. When dealing with continuous variables accuracy is assessed by comparing the observations and predictions for all reference set elements using the Root Mean Squared Errors or an error similar measure [Franco-Lopez et al, 2001]. …”

Section: The K-nn Algorithmmentioning

confidence: 99%

K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by thek-Nearest Neighbors algorithm

Chirici

Corona

Marchetti

et al. 2012

European Journal of Remote Sensing

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Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method

Cited by 402 publications

References 21 publications

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Serviks Kanserinin Erken Teşhisi için Çok Katmanlı Sitoloji Küplerinde Çekirdek ve Sitoplazma Bölütlenmesi

K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by thek-Nearest Neighbors algorithm

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