Import of kNN - Revision history

Peder at 15:42, 29 December 2009

2009-12-29T15:42:14Z

Hah at 08:02, 9 September 2009

2009-09-09T08:02:09Z

Hah at 07:29, 9 September 2009

2009-09-09T07:29:58Z

Hah at 07:25, 9 September 2009

2009-09-09T07:25:51Z

Hah at 07:00, 9 September 2009

2009-09-09T07:00:53Z

Hah at 15:01, 8 September 2009

2009-09-08T15:01:01Z

Hah at 14:45, 8 September 2009

2009-09-08T14:45:12Z

Hah: New page: '''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states.

2009-09-08T14:06:05Z

New page: '''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states.

New page

'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states.

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		+	[[Category:User's Guides\|PlanStart\|PlanStart]]
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	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In a feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).		'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In a feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).

← Older revision		Revision as of 08:02, 9 September 2009
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−	By ''k''NN imputation the forest state of a stand is described by sample plots, seemingly field inventoried in the current stand. Some things give the hoax away though; the within-stand variation regarding volume, age, etc. might be either ~~unrealisticly~~ low (if the imputation is done "with replacement", i.e. one reference plot can represent a stand more than once) or ~~unrealisticly~~ high (e.g., Maturity class K1 and S1 should not occur in the same stand, such stand should in real life be split into two stands).	+	By ''k''NN imputation the forest state of a stand is described by sample plots, seemingly field inventoried in the current stand. Some things give the hoax away though; the within-stand variation regarding volume, age, etc. might be either unrealistically low (if the imputation is done "with replacement", i.e. one reference plot can represent a stand more than once) or unrealistically high (e.g., Maturity class K1 and S1 should not occur in the same stand, such stand should in real life be split into two stands).

← Older revision		Revision as of 07:29, 9 September 2009
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−	By ''k''NN imputation the forest state of a stand is described by sample plots, seemingly field inventoried in the current stand. Some things give the hoax away though; the within-stand variation regarding volume, age, etc. might be ~~both~~ unrealisticly low (if the imputation is done "with replacement", i.e. one reference plot can represent a stand more than once) or unrealisticly high (e.g., Maturity class K1 and S1 should not occur in the same stand, such stand should in real life be split into two stands).	+	By ''k''NN imputation the forest state of a stand is described by sample plots, seemingly field inventoried in the current stand. Some things give the hoax away though; the within-stand variation regarding volume, age, etc. might be either unrealisticly low (if the imputation is done "with replacement", i.e. one reference plot can represent a stand more than once) or unrealisticly high (e.g., Maturity class K1 and S1 should not occur in the same stand, such stand should in real life be split into two stands).

← Older revision	Revision as of 07:25, 9 September 2009
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	+
	+	By ''k''NN imputation the forest state of a stand is described by sample plots, seemingly field inventoried in the current stand. Some things give the hoax away though; the within-stand variation regarding volume, age, etc. might be both unrealisticly low (if the imputation is done "with replacement", i.e. one reference plot can represent a stand more than once) or unrealisticly high (e.g., Maturity class K1 and S1 should not occur in the same stand, such stand should in real life be split into two stands).

← Older revision		Revision as of 07:00, 9 September 2009
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−	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In ~~the~~ feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).	+	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In a feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).

−	However, such mean values are not sufficient for the long-term prognoses and the advanced forest management simulations to be analysed by the system. Single tree-data is here needed, preferably as a sample of plots with trees (or seedlings or saplings).	+	However, such mean values are not sufficient for the long-term prognoses and the advanced forest management simulations to be analysed by the system. Single tree-data is here needed, preferably as a sample of plots with trees (or seedlings or saplings).

← Older revision		Revision as of 15:01, 8 September 2009
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	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In the feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).		'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In the feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).
		+
		+	However, such mean values are not sufficient for the long-term prognoses and the advanced forest management simulations to be analysed by the system. Single tree-data is here needed, preferably as a sample of plots with trees (or seedlings or saplings).

← Older revision		Revision as of 14:45, 8 September 2009
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−	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states.	+	'''The ''k''NN-Sweden data''' consist of pixelwise estimates of forest states. A complete coverage is possible by using remotely sensed information, usually satellite image data. Sensors onboard, e.g., a SPOT satellite obtain spectral signatures in different wavelength bands (usually IR, R, G, and B) in each pixel, with a ground resolution approx. corresponding to 25 by 25 meters. Geo-referenced, previously inventoried NFI-plots will serve as reference data. In the feature space, defined by the satellite image, the ''k'' Nearest Neighbours (usually with ''k'' = 10) are found among the reference plots. Estimations of volume, age, tree species composition, etc. are then derived as weighted mean values (e.g. weighted inversely proportional to the distance in the feature space).