Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Sari, Inggit Lolita; Weston, Christopher J.; Newnham, Glenn; Volkova, Liubov

doi:10.3390/rs14010003

Cited by 10 publications

(5 citation statements)

References 58 publications

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“…The average NDMI value was identified as 0.55 for Landsat-7 ETM+ image and 0.80 for Landsat-8 OLI image. These results indicated that the vegetation was healthy and had a dense canopy cover [73,90]. However, non-mangrove pixels remained, particularly in the terrestrial vegetation region, but were still grouped into the mangrove class.…”

Section: Distribution Of Mangrove Forests In 2002mentioning

confidence: 90%

“…Decision trees have been widely used for land cover classification, particularly with remote sensing technology approaches [70], and are often used to integrate multi-source data, such as optical imagery and SAR [71][72][73]. Several previous studies have also developed a DT algorithm to identify the presence of mangrove forests [21,26].…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

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Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

et al. 2022

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Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI+NDSI+DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI+NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI+NDSI+DEM parameters with the number of trees = 100, mtry = all variables (, and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

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Section: Distribution Of Mangrove Forests In 2002mentioning

confidence: 90%

Section: Decision Tree Algorithmmentioning

confidence: 99%

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

et al. 2022

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“…Spectral-temporal data were extracted from the reference dataset at these points, and three specific indices were applied to analyze and interpret the data. These indices (VH/VV ratio, NDVI, NDMI) are widely used in the literature to define phenological stages and biophysical parameters of vegetation [17,86,87]. The VH/VV ratio in decibels is given by: VH/VV ratio (dB) = VH (dB) − VV (dB)…”

Section: Hazelnut Groves Characterizationmentioning

confidence: 99%

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Lodato,

Pennazza,

Santonico

et al. 2024

Remote Sensing

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The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system.

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“…It was observed that there is misclassification of natural forests and human raised plantations like oil palm, and rubber in Ghana and Indonesia as cited by Jacob Abramowitz et al [10] and Inggit et al [11], respectively. In Bangladesh, a standard classification system was introduced to provide consistent Land cover information [12].…”

Section: Introductionmentioning

confidence: 99%

Agroforestry mapping using multi temporal hybrid CNN+LSTM framework with landsat 8 satellite imagery and google earth engine

Vincent M,

Varalakshmi

2024

Environ. Res. Commun.

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Agroforestry is indeed a traditional practice followed in tropicalcountries like India. About 28.43 million hectare area is used for agroforestcultivation. By 2050 India has the mission of increasing the area underagroforestry to 53 million hectares. In this study, we have made an effort to mapthe agroforest areas using the geospatial tools and hybrid deep learning techniques.The land utilized for cultivation and various agroforestry activities such as rubber,tea, coconut, and banana plantation were classified as forest canopy by the existingclassifiers taking the tree canopy density as a parameter. In light of proposinga solution to the issue, we have put forth a multi temporal hybrid deep learningframework which is a fusion of convolutional neural network, a deep neural netand long short term memory network to classify agroforestry distinguishing itfrom the forest canopy using remote sensing data. The experimentation wascarried out in the southern districts of India, and Landsat 8 imagery was used toclassify the agroforestry of the study area that includes tea, banana, rubber,coconut, and crop lands. An efficient multi temporal hybrid deep learningframework was designed to classify the agroforest plantation distinguishing itfrom crop lands and forest clusters. The experimental results of multi temporalhybrid CNN+LSTM outperformed CNN, LSTM, BiLSTM model reducing theerror rate with respective accuracy and kappa score of 98.23% and 0.88. Theproposed method provides a benchmark to accurately classify and estimate theLULC, particularly mapping the agroforest plantation for other regions across thecountry.

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Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Cited by 10 publications

References 58 publications

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Agroforestry mapping using multi temporal hybrid CNN+LSTM framework with landsat 8 satellite imagery and google earth engine

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