Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia

International audienceThis study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations

Editorial for Special Issue: “Remote Sensing of Forest Cover Change”

Forests play a critical role in the global carbon budget, either acting as a sink of carbon from growth processes (e. [...

Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam

This study aimed to map the status of mangrove forests over the coasts of Hai Ha District and Mong Cai City in Quang Ninh Province by using 2019 Landsat-8 imagery. It then developed the AGB estimation model of mangrove forests based on the AGB estimation-derived plots inventory and vegetation indices-derived from Landsat-8 data. As results, there were five land covers identified, including mangrove forests, other vegetation, wetlands, built-up, and water, with the overall accuracy assessments of 80.0% and Kappa coefficient of 0.74. The total extent of mangrove forests was estimated at 4291.2 ha. The best AGB estimation model that was selected to estimate the AGB and AGC of mangrove forests for the whole coasts of Hai Ha District and Mong Cai City is AGB= 30.38 + 911.95*SAVI (R2=0.924, PValue <0.001). The model validation assessment has confirmed that the selected AGB model can be applied to Hai Ha and Mong Cai coasts with the mean difference between AGB observed and AGB predicted at 16.0 %. This satisfactory AGB model also suggests a good potential for AGB and AGC mapping, which offer the carbon trading market in the study site. As the AGB model selected, the total AGB and AGC of mangrove forests were estimated at about 14,600,000 tons and 6,868,076 tons with a range of from 94.0 - 432.0 tons ha-1, from 44.2 - 203.02 tons ha-1, respectively. It also suggests that the newly-developed AGB model of mangrove forests can be used to estimate AGC stocks and carbon sequestration of mangrove forests for C-PFES in over the coasts of Hai Ha District and Mong Cai City, which is a very importantly financial source for mangrove forest managers, in particular for local mangrove protectors

Monitoring vegetation using remote sensing time series data: a review of the period 1996-2017

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources.Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources

Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further researchEkosistem mangrove merupakan kawasan yang sangat potensial, umumnya berada di kawasan ekoton (kombinasi kawasan intertidal dan supratidal), dimana terdapat interaksi antara perairan (laut, air payau, dan sungai) dengan kawasan daratan. Indonesia khususnya wilayah Banten dan Jawa Barat memiliki kawasan mangrove yang sangat luas dan saat ini terancam alih fungsi lahan. Apalagi pemetaan sebaran hutan bakau menggunakan platform Google Earth Engine berbasis Cloud Computing kurang dipublikasikan. Oleh karena itu, penelitian ini dilakukan dengan memperkenalkan sebaran hutan mangrove yang melibatkan metode algoritma klasifikasi Random Forest (RF), dan mencari modifikasi indeks yang terbaik. Uji kombinasi dilakukan dengan melibatkan indeks NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, dan LSWI. Sebaran mangrove terdapat di tiga provinsi (Jawa Barat, Banten, dan DKI Jakarta) yaitu seluas 933,54 ha (8,372%), 1.537,89 ha (18,231%), dan 8.184,82 ha (73,397%). Dari 70 pengujian kombinasi, indeks LSWI (K13, Type-A) merupakan kombinasi dengan tingkat akurasi terendah sebesar 58,45% (Overal Accuracy) dan 39,59 (Kappa statistik), dan kombinasi K23 (SAVI-MNDWI-IBI) merupakan kombinasi yang terbaik yaitu 96,48% dan 92,79. Hasil dan rekomendasi dalam penelitian ini diharapkan dapat digunakan sebagai acuan dalam menentukan kebijakan perlindungan kawasan mangrove dan referensi untuk penelitian selanjutnya

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Applying multi-temporal landsat satellite data and markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.</jats:p

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future