Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality DOI Creative Commons
Mthokozisi Ndumiso Mzuzuwentokozo Buthelezi, Romano Lottering, Kabir Peerbhay

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 36, P. 101335 - 101335

Published: Aug. 28, 2024

Forests are crucial in delivering ecosystem services that underpin human well-being and biodiversity conservation. However, these vital ecosystems threatened by forest degradation rapid urbanisation. This study addresses this challenge proposing a comprehensive framework for mapping natural forests at the municipal scale. The integrates remote sensing techniques with machine learning algorithms to provide valuable insights into extent of within eThekwini Municipality. utilised Landsat 7, 8, 9 satellite imagery analyse map historical current distribution forests. Five spectral indices, namely, Normalized Differential Vegetation Index (NDVI), Green Difference (GNDVI), Chlorophyll (CIG), Enhanced (EVI), Index-2 (EVI-2), which were calculated from bands, employed analysis. Light Gradient Boosting Machine (LightGBM), Categorical (CatBoost), Extreme (XGBoost) used model distribution. Accuracy was assessed through confusion matrices, Receiver Operating Characteristic (ROC) Curves, area under ROC curve (AUC), F1 scores. LightGBM achieved highest overall accuracy (90.76%), followed CatBoost (89.56%) XGBoost (84.34%). also obtained best score (90.76%). These findings highlight LightGBM's effectiveness classifying forests, making it preferred classifications based on 7 significantly underestimated area, whereas 8 data revealed an increase 2015 2023. will guide effective targeted rehabilitation restoration efforts, ensuring preservation enhancement services.

Language: Английский

Estimation of Land Surface Temperature and LULC Changes impact on Groundwater resources in the semi-arid region of Madhya Pradesh, India DOI
Kanak N. Moharir,

Chaitanya Baliram Pande,

Vinay Kumar Gautam

et al.

Advances in Space Research, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

3
Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments DOI

Jiahua Yu,

Qiao Hu, Jiating Li

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 112088 - 112088

Published: Sept. 1, 2024

Language: Английский

Citations

2
A spatio-temporal unmixing with heterogeneity model for the identification of remotely sensed MODIS aerosols: Exemplified by the case of Africa DOI Creative Commons
Longshan Yang,

Peng Luo,

Zehua Zhang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 132, P. 104068 - 104068

Published: Aug. 1, 2024

Aerosols are crucial constituents of the atmosphere, with significant impacts on air quality. Aerosol optical depth (AOD) is critical in assessing solar resources and modeling sky radiance. However, comprehensive aerosol studies at a continental scale limited, existing methodologies need to consider spatial characteristics. This study develops spatio-temporal unmixing heterogeneity (STUH) model evaluate patterns temporal trends atmospheric aerosols across African continent. The AOD data cube, comprising monthly averaged MODIS-derived from 2001 2015, was decomposed using spatially non-negative matrix variabilization explore determinants their interactions geographically optimal zones-based (GOZH) model. Our findings reveal an increasing trend levels Africa past 15 years, combined pattern explained by five abundance variables. We find that different regions Africa, impact natural variables 1.56 3.01 times human variables, variations. These results essential for understanding climatic implications Africa.

Language: Английский

Citations

1
Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model DOI

M. Durairaj,

Kasapaka Rubenraju,

B. Krishna

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: 7(4), P. 261 - 270

Published: Aug. 23, 2024

Language: Английский

Citations

1
Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality DOI Creative Commons
Mthokozisi Ndumiso Mzuzuwentokozo Buthelezi, Romano Lottering, Kabir Peerbhay

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 36, P. 101335 - 101335

Published: Aug. 28, 2024

Forests are crucial in delivering ecosystem services that underpin human well-being and biodiversity conservation. However, these vital ecosystems threatened by forest degradation rapid urbanisation. This study addresses this challenge proposing a comprehensive framework for mapping natural forests at the municipal scale. The integrates remote sensing techniques with machine learning algorithms to provide valuable insights into extent of within eThekwini Municipality. utilised Landsat 7, 8, 9 satellite imagery analyse map historical current distribution forests. Five spectral indices, namely, Normalized Differential Vegetation Index (NDVI), Green Difference (GNDVI), Chlorophyll (CIG), Enhanced (EVI), Index-2 (EVI-2), which were calculated from bands, employed analysis. Light Gradient Boosting Machine (LightGBM), Categorical (CatBoost), Extreme (XGBoost) used model distribution. Accuracy was assessed through confusion matrices, Receiver Operating Characteristic (ROC) Curves, area under ROC curve (AUC), F1 scores. LightGBM achieved highest overall accuracy (90.76%), followed CatBoost (89.56%) XGBoost (84.34%). also obtained best score (90.76%). These findings highlight LightGBM's effectiveness classifying forests, making it preferred classifications based on 7 significantly underestimated area, whereas 8 data revealed an increase 2015 2023. will guide effective targeted rehabilitation restoration efforts, ensuring preservation enhancement services.

Language: Английский

Citations

1