Study on Global Burned Forest Areas Based on Landsat Data

2020 ◽  
Vol 86 (8) ◽  
pp. 503-508
Author(s):  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Guojin He ◽  
Mingyue Wei ◽  
Chao Tang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Cover ◽  
Time Series Data ◽  
Global Climate ◽  
Distribution Patterns ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Series Data ◽  
Burned Area ◽  
Burned Forest

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

scholarly journals Biecological Carrying Capacity of Forest and Land in Manokwari Regency, West Papua Province

2020 ◽  
Vol 8 (2) ◽  
pp. 197
Author(s):  
Jonni Marwa ◽  
Anton Silas Sineri ◽  
Francine Hematang
Keyword(s):  
Carrying Capacity ◽  
Ecological Footprint ◽  
Forest Cover ◽  
Time Series Data ◽  
Forest Degradation ◽  
High Rate ◽  
Series Data ◽  
Capacity Change ◽  
Quantitative Descriptive ◽  
Forest Lands

Manokwari Regency has high environmental risks due to the high rate of population growth and increased migration. This condition could affect the biocapacity of forest and land resources as well as the ecological footprint that could fulfill the needs of the community in the Manokwari Regency. This study aimed to assess the bioecological carrying capacity of forest and land, changes in forest cover, and projecting the bioecological carrying capacity for the next 50 years in the Manokwari Regency. A quantitative descriptive approach based on secondary time series data analysis and land cover dynamics analysis was used. The ecological footprint approach was carried out by calculating the ecological footprint, biocapacity, and bioecological carrying capacity. The results showed that the bioecological carrying capacity in 2017 in Manokwari District decreased compared to 2012. Forest degradation tended to decrease at a rate of 372 ha/year. However, deforestation increased at a rate of 1,298 ha/year. The results indicated that the policy of converting forests to permanent non-forest lands in the last five years was very massive. The projected bioecological carrying capacity in the next 50 years showed that forest and land in Manokwari District tend to be overshoot.Keywords: bioecological carrying capacity, change in land use, forests, land, Manokwari

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

scholarly journals An External Agribusiness Risk Analysis Using KBDI: A Case of Veldfires in the Northern Territory of Australia

2020 ◽  
Vol 3 (1) ◽  
pp. 37
Author(s):  
Toyi Maniki Diphagwe ◽  
Bernard Moeketsi Hlalele ◽  
Dibuseng Priscilla Mpakathi
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Crop Production ◽  
Time Series Data ◽  
Northern Territory ◽  
Return Level ◽  
Series Data ◽  
Burned Area ◽  
Trend Test ◽  
P Value

The 2019/20 Australian bushfires burned over 46 million acres of land, killed 34 people and left 3500 individuals homeless. Majority of deaths and buildings destroyed were in New South Wales, while the Northern Territory accounted for approximately 1/3 of the burned area. Many of the buildings that were lost were farm buildings, adding to the challenge of agricultural recovery that is already complex because of ash-covered farmland accompanied by historic levels of drought. The current research therefore aimed at characterising veldfire risk in the study area using Keetch-Byram Drought Index (KBDI). A 39-year-long time series data was obtained from an online NASA database. Both homogeneity and stationarity tests were deployed using a non-parametric Pettitt’s and Dicky-Fuller tests respectively for data quality checks. Major results revealed a non-significant two-tailed Mann Kendall trend test with a p-value = 0.789 > 0.05 significance level. A suitable probability distribution was fitted to the annual KBDI time series where both Kolmogorov-Smirnov and Chi-square tests revealed Gamma (1) as a suitably fitted probability distribution. Return level computation from the Gamma (1) distribution using XLSTAT computer software resulted in a cumulative 40-year return period of moderate to high fire risk potential. With this low probability and 40-year-long return level, the study found the area less prone to fire risks detrimental to animal and crop production. More agribusiness investments can safely be executed in the Northern Territory without high risk aversion.

scholarly journals A river system modelling platform for Murray-Darling Basin, Australia

2012 ◽  
Vol 15 (4) ◽  
pp. 1109-1120 ◽  
Author(s):  
Ang Yang ◽  
Geoff Podger ◽  
Shane Seaton ◽  
Robert Power
Keyword(s):  
Time Series Data ◽  
Local Development ◽  
Global Climate ◽  
Sustainable Use ◽  
River System ◽  
Series Data ◽  
System Modelling ◽  
Hydrological Simulation ◽  
Modelling Framework ◽  
Murray Darling Basin

Global climate change and local development make water supply one of the most vulnerable sectors in Australia. The Australian government has therefore commissioned a series of projects to evaluate water availability and the sustainable use of water resources in Australia. This paper discusses a river system modelling platform that has been used in some of these nationally significant projects. The platform consists of three components: provenance, modelling engine and reporting database. The core component is the modelling engine, an agent-based hydrological simulation system called the Integrated River System Modelling Framework (IRSMF). All configuration information and inputs to IRSMF are recorded in the provenance component so that modelling processes can be reproduced and results audited. The reporting database is used to store key statistics and raw output time series data for selected key parameters. This river system modelling platform has for the first time modelled a river system at the basin level in Australia. It provides practitioners with a unique understanding of the characteristics and emergent behaviours of river systems at the basin level. Although the platform is purpose-built for the Murray-Darling Basin, it would be easy to apply it to other basins by using different river models to model agent behaviours.

scholarly journals CNN SEMANTIC SEGMENTATION TO RETRIEVE PAST LAND COVER OUT OF HISTORICAL ORTHOIMAGES AND DSM: FIRST EXPERIMENTS

2020 ◽  
Vol V-2-2020 ◽  
pp. 1013-1019
Author(s):  
A. Le Bris ◽  
S. Giordano ◽  
C. Mallet
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Time Series Data ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Training Data ◽  
Training Dataset ◽  
Series Data ◽  
Temporal Sampling ◽  
3D Information

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets.

scholarly journals Forest Monitoring Using Landsat Time Series Data: A Review

2014 ◽  
Vol 40 (5) ◽  
pp. 362-384 ◽  
Author(s):  
Asim Banskota ◽  
Nilam Kayastha ◽  
Michael J. Falkowski ◽  
Michael A. Wulder ◽  
Robert E. Froese ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Monitoring ◽  
Series Data
Sign in / Sign up

Export Citation Format

Share Document