Deforestation mapping sampling designs for Canadian landscapes
Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canadaâs forest and the rare and spatially diverse nature of its deforestation, a sampling appro...
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| Published in | Canadian journal of forest research Vol. 45; no. 11; pp. 1564 - 1576 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Ottawa
NRC Research Press
01.11.2015
Canadian Science Publishing NRC Research Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1208-6037 0045-5067 1208-6037 |
| DOI | 10.1139/cjfr-2014-0541 |
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| Abstract | Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canadaâs forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard errorâ/âsample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%â25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%â40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation. |
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| AbstractList | Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada’s forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error / sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%–25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%–40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation. Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada's forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error/sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%-25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%-40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation. Key words: deforestation, sample design, monitoring, greenhouse gas emissions, CBM-CFS3. La deforestation est la conversion anthropique directe de la foret a des usages non forestiers des terres. Il est important pour les nations de comprendre et de rapporter l'ampleur de leur deforestation. A cause de l'immensite de la foret canadienne et etant donne la rarete et la diversite spatiale de sa deforestation, il etait necessaire de mettre au point une methode d'echantillonnage qui permet de cartographier la deforestation, puis de changer d'echelle pour representer la deforestation dans differentes regions. L'efficacite de differents plans d'echantillonnage a determiner l'aire de deforestation a ete evaluee a l'aide d'une methode de Monte Carlo dans laquelle differents plans d'echantillonnage ont ete appliques pour simuler des paysages forestiers representatifs de differentes regions et de divers schemas de deforestation au Canada. Comme mesure de la performance du plan d'echantillonnage, nous avons utilise l'erreur d'echantillonnage exprimee par l'erreur-type du niveau de deforestation estime pour l'echantillon, divisee par la deforestation reelle du paysage simule. Les resultats indiquent que l'erreur d'echantillonnage etait dependante des caracteristiques de la deforestation (c.-a-d. la quantite, la forme, la taille et la distribution). Par exemple, avec l'augmentation de la taille moyenne des evenements ou la diminution de la proportion des evenements de deforestation lineaire (c.-a-d. les routes et les corridors), l'intensite d'echantillonnage requise pour atteindre un certain niveau d'erreur d'echantillonnage augmente et les paysages avec un petit nombre d'evenements tres vastes necessitaient la plus forte intensite d'echantillonnage. Pour atteindre une cible d'erreur relative d'echantillonnage (rapport de l'erreur-type sur la moyenne de l'echantillon) de 10 % a l'aide de plans d'echantillonnage systematique en grille avec des placettes carrees, un echantillon de 15 a 25 % d'un paysage sera necessaire pour la plupart des paysages canadiens avec un calendrier cartographique de 10 ans (intervalle de temps entre les echantillons) et en assumant un taux de deforestation annuel de 0,025 %. Si la cartographie est realisee sur une periode de cinq ans, l'intensite d'echantillonnage requise augmente et se situe entre 20 et 40 %. Nous discutons aussi des consequences de l'erreur d'echantillonnage de differents plans sur l'incertitude des emissions estimees de gaz a effet de serre provenant de la deforestation. [Traduit par la Redaction] Mots-cles: deforestation, plan d'echantillonnage, surveillance, emissions de gaz a effet de serre, CBM-CFS3. Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada's forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error/sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%-25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%-40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation.Original Abstract: La deforestation est la conversion anthropique directe de la foret a des usages non forestiers des terres. Il est important pour les nations de comprendre et de rapporter l'ampleur de leur deforestation. A cause de l'immensite de la foret canadienne et etant donne la rarete et la diversite spatiale de sa deforestation, il etait necessaire de mettre au point une methode d'echantillonnage qui permet de cartographier la deforestation, puis de changer d'echelle pour representer la deforestation dans differentes regions. L'efficacite de differents plans d'echantillonnage a determiner l'aire de deforestation a ete evaluee a l'aide d'une methode de Monte Carlo dans laquelle differents plans d'echantillonnage ont ete appliques pour simuler des paysages forestiers representatifs de differentes regions et de divers schemas de deforestation au Canada. Comme mesure de la performance du plan d'echantillonnage, nous avons utilise l'erreur d'echantillonnage exprimee par l'erreur-type du niveau de deforestation estime pour l'echantillon, divisee par la deforestation reelle du paysage simule. Les resultats indiquent que l'erreur d'echantillonnage etait dependante des caracteristiques de la deforestation (c.-a-d. la quantite, la forme, la taille et la distribution). Par exemple, avec l'augmentation de la taille moyenne des evenements ou la diminution de la proportion des evenements de deforestation lineaire (c.-a-d. les routes et les corridors), l'intensite d'echantillonnage requise pour atteindre un certain niveau d'erreur d'echantillonnage augmente et les paysages avec un petit nombre d'evenements tres vastes necessitaient la plus forte intensite d'echantillonnage. Pour atteindre une cible d'erreur relative d'echantillonnage (rapport de l'erreur-type sur la moyenne de l'echantillon) de 10 % a l'aide de plans d'echantillonnage systematique en grille avec des placettes carrees, un echantillon de 15 a 25 % d'un paysage sera necessaire pour la plupart des paysages canadiens avec un calendrier cartographique de 10 ans (intervalle de temps entre les echantillons) et en assumant un taux de deforestation annuel de 0,025 %. Si la cartographie est realisee sur une periode de cinq ans, l'intensite d'echantillonnage requise augmente et se situe entre 20 et 40 %. Nous discutons aussi des consequences de l'erreur d'echantillonnage de differents plans sur l'incertitude des emissions estimees de gaz a effet de serre provenant de la deforestation. [Traduit par la Redaction] Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canadaâs forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard errorâ/âsample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%â25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%â40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation. |
| Abstract_FL | La déforestation est la conversion anthropique directe de la forêt à des usages non forestiers des terres. Il est important pour les nations de comprendre et de rapporter l’ampleur de leur déforestation. À cause de l’immensité de la forêt canadienne et étant donné la rareté et la diversité spatiale de sa déforestation, il était nécessaire de mettre au point une méthode d’échantillonnage qui permet de cartographier la déforestation, puis de changer d’échelle pour représenter la déforestation dans différentes régions. L’efficacité de différents plans d’échantillonnage à déterminer l’aire de déforestation a été évaluée à l’aide d’une méthode de Monte Carlo dans laquelle différents plans d’échantillonnage ont été appliqués pour simuler des paysages forestiers représentatifs de différentes régions et de divers schémas de déforestation au Canada. Comme mesure de la performance du plan d’échantillonnage, nous avons utilisé l’erreur d’échantillonnage exprimée par l’erreur-type du niveau de déforestation estimé pour l’échantillon, divisée par la déforestation réelle du paysage simulé. Les résultats indiquent que l’erreur d’échantillonnage était dépendante des caractéristiques de la déforestation (c.-à-d. la quantité, la forme, la taille et la distribution). Par exemple, avec l’augmentation de la taille moyenne des événements ou la diminution de la proportion des événements de déforestation linéaire (c.-à-d. les routes et les corridors), l’intensité d’échantillonnage requise pour atteindre un certain niveau d’erreur d’échantillonnage augmente et les paysages avec un petit nombre d’événements très vastes nécessitaient la plus forte intensité d’échantillonnage. Pour atteindre une cible d’erreur relative d’échantillonnage (rapport de l’erreur-type sur la moyenne de l’échantillon) de 10 % à l’aide de plans d’échantillonnage systématique en grille avec des placettes carrées, un échantillon de 15 à 25 % d’un paysage sera nécessaire pour la plupart des paysages canadiens avec un calendrier cartographique de 10 ans (intervalle de temps entre les échantillons) et en assumant un taux de déforestation annuel de 0,025 %. Si la cartographie est réalisée sur une période de cinq ans, l’intensité d’échantillonnage requise augmente et se situe entre 20 et 40 %. Nous discutons aussi des conséquences de l’erreur d’échantillonnage de différents plans sur l’incertitude des émissions estimées de gaz à effet de serre provenant de la déforestation. [Traduit par la Rédaction] |
| Audience | Academic |
| Author | Leckie, Donald G Werner A. Kurz Steen Magnussen Dennis Paradine |
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| SubjectTerms | Canada CBM-CFS3 Deforestation déforestation Environmental aspects Forest management forests greenhouse gas emissions Greenhouse gases Land use landscapes Mapping monitoring Monte Carlo simulation plan d’échantillonnage roads sample design Sampling surveillance Uncertainty émissions de gaz à effet de serre |
| Title | Deforestation mapping sampling designs for Canadian landscapes |
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