Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery

Jiang, Xiandie; Lu, Dengsheng; Moran, Emílio F.; Calvi, Miquéias Freitas; Dutra, Luciano Vieira; Li, Guiying

doi:10.1016/j.apgeog.2018.05.019

Cited by 51 publications

(48 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Firstly, the temporal mismatch of remote sensing and field survey data due to the lack of cloud free Landsat scenes corresponding to the exact times of ground observation can lead to estimation error in the results. Furthermore, different types of forest surfaces have similar spectral signatures, which could have resulted in misclassification [77]. The verification accuracy of the classification results indicates that the BMF is the main reason for the precision declining.…”

Section: Discussionmentioning

confidence: 97%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

View full text Add to dashboard Cite

Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

show abstract

Section: Discussionmentioning

confidence: 97%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

View full text Add to dashboard Cite

show abstract

“…In addition, accuracy verification results of the land use maps showed that BFM was the major reason for the overall decrease in accuracy. This is because different impervious surfaces have similar spectral signatures, which can result in misclassification [71]. The spectral characteristics of BMF are close to those of BLF and CNF, resulting in a high degree of error in the process of manual visual interpretation and selection of training samples for the establishment of the decision tree.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Zhang

Mao

et al. 2020

IJGI

View full text Add to dashboard Cite

Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.

show abstract

“…Um exemplo é fornecido pela redução de áreas de unidades de conservação existentes para abrir caminho para as seis primeiras barragens propostas na bacia do rio Tapajós (FEARNSIDE, 2015a). Além da perda das florestas a serem inundadas, as represas estimulam o desmatamento nas áreas circundantes (e.g., BARRETO et al, 2011;JIANG et al, 2018).…”

Section: Impactos Das Barragens Na Amazônia: Terras Inundadas E Populunclassified

Impactos das hidrelétricas na Amazônia e a tomada de decisão

Fearnside¹

2019

NCN

View full text Add to dashboard Cite

As barragens brasileiras na Amazônia têm grandes impactos sociais e ambientais, que são sistematicamente subestimados nos Estudos de Impacto Ambiental (EIA) no processo de licenciamento. A equipe técnica no IBAMA, responsável pelo licenciamento, tem sido ignorado para aprovar uma série de barragens, apesar dos altos impactos, da falta de consulta aos povos indígenas e dos EIA inadequados. Há projetos de lei e propostas de emendas constitucionais que ameaçam a desconfirurar ou até eliminar o sistema de licenciamento como um todo. O uso de “suspensões de segurança” neutraliza, em grande parte, o sistema judicial em seus esforços para fazer cumprir a lei que exige a consulta aos povos indígenas ou para obrigar o cumprimento das exigências do licenciamento ambiental. A gravidade deste quadro é evidente, mas entendê-lo é o primeiro passo para que sejam mudados os sistemas de tomada de decisão e de licenciamento que levam aos impactos ilustrados pela história recente na Amazônia. O Brasil dispõe de amplas opções energéticas com menos impactos socioambientais do que as barragens que recebem prioridade hoje.

show abstract

Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery

Cited by 51 publications

References 34 publications

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Impactos das hidrelétricas na Amazônia e a tomada de decisão

Contact Info

Product

Resources

About