Multi-sensor forest vegetation height mapping methods for Tanzania

Trier, Øivind Due; Salberg, Arnt-Børre; Haarpaintner, Jörg; Aarsten, Dagrun; Gobakken, Terje; Næsset, Erik

doi:10.1080/22797254.2018.1461533

Cited by 14 publications

(10 citation statements)

References 35 publications

(34 reference statements)

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“…However, limitations related to data availability, high cost and the volume [5,6] of commercial high-resolution remote-sensing data impede their operational forestry applications in low income countries. Thus, there is a need to develop cost-efficient and affordable methods, which provide successful forest volume and biomass estimates in various environments [7].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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“…A combination of methods of remote sensing techniques and Geographic Information Systems (GIS) is commonly used to explore details about forest ecology globally (e.g., Hadi et al, 2018;Trier et al, 2018) and in Bangladesh (e.g., Braun et al, 2019;Imtiaz, 2018;Islam et al, 2019;Islam, 2014). In this combined method, the Normalized Difference Vegetation Index (NDVI) (Campbell, 2002, p. 466) is applied.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the domestic energy use impacts of unplanned refugee settlements on the forest ecology of Cox’s Bazar, Bangladesh

Islam

Hemstock²,

Charlesworth

et al. 2022

Ecocycles

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This study monitors the forest ecology in Himchari National Park, Teknaf Wildlife Sanctuary, and between the areas in Cox's Bazar district, Bangladesh. The area has a rich biodiversity, including globally endangered species such as Asian Elephants (Elephas maximus) and Boilam Trees (Anisoptera scaphula), which are threatened by anthropogenic development, newly refugees’ unplanned settlements and their use of domestic energy. Geographic Information System (GIS) and Landsat satellite images are used to monitor forest coverage for 1995–2018. The Normalized Difference Vegetation Index (NDVI) is applied to quantify forest area. Focus group discussions and questionnaire surveys were conducted to reveal stakeholder perceptions about their dependency on forest resources as ecosystem services. Close to the refugee camp areas, the forest coverage changes to grassland due to the unsustainable forest resource extraction. Despite the free of charge supply of Liquefied Petroleum Gas, the refugees burn 2,380 metric tons of firewood every month to satisfy energy for cooking. Besides, 200,000 households frequently use bamboo, small trees, and shrubs to maintain their dwellings' structure. Thus, deforestation caused by immigration between 2017–2018 is similar to that caused by the effect of climate change, including severe tropical cyclones in 1994–1995. This research identifies domestic energy supply deficiency and impacts, and the need for comparatively durable housing materials to reduce stress on forest resources and health hazards.

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“…Several studies have highlighted the advantages of remote sensing in long-term monitoring projects, focusing on different aspects, as some researchers used spectral measurements derived from numerous satellite sensors, such as vegetation indices (VIs), to evaluate vegetation coverage [ 26 , 27 , 28 ]. Other studies have focused on tracking the seasonal dynamics of vegetation [ 29 ], detecting vegetation and land changes [ 30 ], and assessing ecological revegetation and restoration projects [ 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, monitoring the growth of desert plants using UAVs remains a challenge due to the small vegetation structure of plant communities. Previous studies proposed many remote sensing methods for estimating vegetation height [ 36 ]. Unfortunately, most studies have used complicated and expensive methodologies, such as light detection and ranging (LIDAR) [ 34 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

Abdullah

Al-Ali²,

Abdullah

et al. 2021

Plants

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The rapid assessment and monitoring of native desert plants are essential in restoration and revegetation projects to track the changes in vegetation patterns in terms of vegetation coverage and structure. This work investigated advanced vegetation monitoring methods utilizing UAVs and remote sensing techniques at the Al Abdali protected site in Kuwait. The study examined the effectiveness of using UAV techniques to assess the structure of desert plants. We specifically examined the use of very-high-resolution aerial imagery to estimate the vegetation structure of Rhanterium epapposum (perennial desert shrub), assess the vegetation cover density changes in desert plants after rainfall events, and investigate the relationship between the distribution of perennial shrub structure and vegetation cover density of annual plants. The images were classified using supervised classification techniques (the SVM method) to assess the changes in desert plants after extreme rainfall events. A digital terrain model (DTM) and a digital surface model (DSM) were also generated to estimate the maximum shrub heights. The classified imagery results show that a significant increase in vegetation coverage occurred in the annual plants after rainfall events. The results also show a reasonable correlation between the shrub heights estimated using UAVs and the ground-truth measurements (R² = 0.66, p < 0.01). The shrub heights were higher in the high-cover-density plots, with coverage >30% and an average height of 77 cm. However, in the medium-cover-density (MD) plots, the coverage was <30%, and the average height was 52 cm. Our study suggests that utilizing UAVs can provide several advantages to critically support future ecological studies and revegetation and restoration programs in desert ecosystems.

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Multi-sensor forest vegetation height mapping methods for Tanzania

Cited by 14 publications

References 35 publications

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Assessment of the domestic energy use impacts of unplanned refugee settlements on the forest ecology of Cox’s Bazar, Bangladesh

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

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