Forest Condition Monitoring Using Very-High-Resolution Satellite Imagery in a Remote Mountain Watershed in Nepal

Uddin, Kabir; Gilani, Hammad; Murthy, M. S. R.; Kotru, Rajan; Qamer, Faisal Mueen

doi:10.1659/mrd-journal-d-14-00074.1

Cited by 23 publications

(16 citation statements)

References 67 publications

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“…Our results on various degree of tree cover loss and gain at district level correspond with the localized studies, which found decrease in forest cover in some areas (Uddin et al, 2015a;Uddin et al, 2015b) as well as increase in forest cover in others (Niraula et al, 2013;Paudel et al, 2015).…”

Section: Discussionsupporting

confidence: 89%

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Supplemental Information 2: Comparison of Multi-Temporal High Resolution Images of Google Earth Showing Changes in Tree Cover C

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Despite the local and global importance of forests, deforestation driven by various socioeconomic and biophysical factors continues in many countries. In Nepal, in response to massive deforestation, the community forestry program has been implemented to reduce deforestation and support livelihoods. After four decades of its inception, the effectiveness of this program on forest cover change remains mostly unknown. This study analyses the spatial and temporal patterns of tree cover change along with a few socioeconomic drivers of tree cover change to examine the effectiveness of the community forestry program for conserving forests or in reducing deforestation. We also investigate the socioeconomic factors and policy responses as manifested through the community forestry program responsible for the tree cover change at the district level. The total tree cover area in the year 2000 in Nepal was ~ 4,746,000 hectares, and our analysis reveals that between 2001 and 2016, Nepal has lost ~46,000 ha and gained ~12,300 ha of areas covered by trees with a substantial spatial and temporal variations. After accounting socioeconomic drivers of forest cover change, our analysis showed that districts with the larger number of community forests had a minimum loss in tree cover, while districts with higher proportion of vegetation covered by community forests had a maximum gain in tree cover. This indicates a positive contribution of the community forestry program to reducing deforestation and increasing tree cover.

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Section: Discussionsupporting

confidence: 89%

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Supplemental Information 2: Comparison of Multi-Temporal High Resolution Images of Google Earth Showing Changes in Tree Cover C

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“…Forest mapping over large spatial extents with VHR satellites started with IKONOS in 1999 [6,7]. Since then, several VHR satellites have been launched such as QuickBird, GeoEye-1, WorldView-1, -2, and -3, Ziyuan-3A, and the Pléiades satellites.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions

et al. 2018

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This work focuses on the accuracy estimation of canopy height models (CHMs) derived from image matching of Pléiades stereo imagery over forested mountain areas. To determine the height above ground and hence canopy height in forest areas, we use normalised digital surface models (nDSMs), computed as the differences between external high-resolution digital terrain models (DTMs) and digital surface models (DSMs) from Pléiades image matching. With the overall goal of testing the operational feasibility of Pléiades images for forest monitoring over mountain areas, two questions guide this work whose answers can help in identifying the optimal acquisition planning to derive CHMs. Specifically, we want to assess (1) the benefit of using tri-stereo images instead of stereo pairs, and (2) the impact of different viewing angles and topography. To answer the first question, we acquired new Pléiades data over a study site in Canton Ticino (Switzerland), and we compare the accuracies of CHMs from Pléiades tri-stereo and from each stereo pair combination. We perform the investigation on different viewing angles over a study area near Ljubljana (Slovenia), where three stereo pairs were acquired at one-day offsets. We focus the analyses on open stable and on tree covered areas. To evaluate the accuracy of Pléiades CHMs, we use CHMs from aerial image matching and airborne laser scanning as reference for the Ticino and Ljubljana study areas, respectively. For the two study areas, the statistics of the nDSMs in stable areas show median values close to the expected value of zero. The smallest standard deviation based on the median of absolute differences (σMAD) was 0.80 m for the forward-backward image pair in Ticino and 0.29 m in Ljubljana for the stereo images with the smallest absolute across-track angle (−5.3°). The differences between the highest accuracy Pléiades CHMs and their reference CHMs show a median of 0.02 m in Ticino with a σMAD of 1.90 m and in Ljubljana a median of 0.32 m with a σMAD of 3.79 m. The discrepancies between these results are most likely attributed to differences in forest structure, particularly tree height, density, and forest gaps. Furthermore, it should be taken into account that temporal vegetational changes between the Pléiades and reference data acquisitions introduce additional, spurious CHM differences. Overall, for narrow forward–backward angle of convergence (12°) and based on the used software and workflow to generate the nDSMs from Pléiades images, the results show that the differences between tri-stereo and stereo matching are rather small in terms of accuracy and completeness of the CHM/nDSMs. Therefore, a small angle of convergence does not constitute a major limiting factor. More relevant is the impact of a large across-track angle (19°), which considerably reduces the quality of Pléiades CHMs/nDSMs.

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“…Multispectral data has not been explored in this research. Another research by [8] used information from spectral bands, shape and color to classify land cover but this research has not explored the spectral index yet. The Ministry of Environment and Forestry in Indonesia recorded the total forest area in Indonesia reaches 125,922,474 hectares in 2018.…”

Section: Introductionmentioning

confidence: 99%

Forest Classification Method Based on Convolutional Neural Networks and Sentinel-2 Satellite Imagery

Miranda¹,

Mutiara²,

Wibowo³

2019

IJFIS

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The objective of this study is to develop a classification method based on convolutional neural network (CNN) and Sentinel-2 satellite imagery including the spectral feature, spectral index and spatial feature together as an input to answer forest monitoring problem. This research also used contextual information on Indonesia National Standard Agency's document for Land cover classification as a baseline for feature extraction to get the appropriate classifier feature. The test set was located in Semarang, Central Java, Indonesia. The research workflow consists of defining forest class based on Indonesia National Standard Agency for Land cover classification, extracting optical image features based on contextual information of the forest class definition, extracting image features from the Sentinel-2 satellite image, and classifying image object features using CNN classifier. Image segmentation produced 1,211 segments/objects by using eCognition software. Subsequently, these objects were used as a dataset. Overall accuracy was used to evaluate the performance of the classification result. The result showed the classification method results in this study yielded high overall accuracy (97.66%) when using CNN with the image features like NDVI, Brightness, GLCM homogeneity and Rectangular fit. Small improvement of overall accuracy was also achieved when it was compared to GBT with an overall accuracy of 95.50%.

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Forest Condition Monitoring Using Very-High-Resolution Satellite Imagery in a Remote Mountain Watershed in Nepal

Cited by 23 publications

References 67 publications

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Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions

Forest Classification Method Based on Convolutional Neural Networks and Sentinel-2 Satellite Imagery

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