A simple method for developing near real-time nationwide forest monitoring for Indonesia using MODIS near- and shortwave infrared bands

Setiawan, Yudi; Kustiyo, Kustiyo; Darmawan, Arief

doi:10.1080/2150704x.2015.1137645

Cited by 9 publications

(19 citation statements)

References 10 publications

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“…In this case, R-band was used to represent spectral absorption by vegetation and the NIR-band to represent the reflectance value (Equation (1)). Therefore, NDVI can characterize the properties of vegetation well [48,49]. NDVI is commonly used to make spatial and temporal comparisons of terrestrial photosynthetic activity; thus, it is one of the indicators of the state of land degradation and of the speed of increase or decrease of photosynthesis, which provides biophysical information [50][51][52].…”

Section: Normalized Indicesmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

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Section: Normalized Indicesmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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“…Several deforestation hotspots in Indonesia for 2020 were detected using the 8-day composite of MODIS at a resolution of 500 m using the algorithm developed by [30]. These were compared with higher-spatial-resolution deforestation hotspots in Indonesia for 2020 based on Landsat-8 OLI data at a resolution of 30 m using the classification method described in Appendix A (see Figure 8).…”

Section: Deforestation Data Setsmentioning

confidence: 99%

“…Deforestation hotspots derived from MODIS were determined by a threshold of change in a composite index, which was calculated using short-wave infrared and nearinfrared bands [30]. Meanwhile, deforestation hotspots derived from Landsat-8 OLI were defined as pixels experiencing land clearing, according to a classification method using red, short-wave infrared, and near-infrared bands.…”

Section: Deforestation Data Setsmentioning

confidence: 99%

“…Meanwhile, deforestation hotspots derived from Landsat-8 OLI were defined as pixels experiencing land clearing, according to a classification method using red, short-wave infrared, and near-infrared bands. Detailed methodology for the deforestation detection method derived from MODIS has been provided elsewhere [30], while a general description of the classification method applied to deforestation detection derived from Landsat-8 OLI is provided in Appendix A. Furthermore, within each deforestation pixel derived from MODIS, with a size of 500 × 500 m 2 , the deforestation intensity was calculated based on Landsat-8 OLI with a pixel size of 30 × 30 m 2 , ranging from 0% to 100%.…”

Section: Deforestation Data Setsmentioning

confidence: 99%

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Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

et al. 2021

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This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.

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“…The pre-processing of MODIS image is executed before a multitemporal transformation, which includes cloud masking, time-series filtering, and interpolation of blank data due to the cloud. Pre-processing is done to minimize the image of the cloud cover [31], [32]. Diagrammatically, the illustration of the paddy field classification model with a phenological model using MODIS-Terra multi-temporal image transformation is illustrated in Figure 2.…”

Section: Image Classificationmentioning

confidence: 99%

Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island

Dimyati¹,

Kustiyo²,

Dimyati³

2019

IJECE

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This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.

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A simple method for developing near real-time nationwide forest monitoring for Indonesia using MODIS near- and shortwave infrared bands

Cited by 9 publications

References 10 publications

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island

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