scholarly journals A long-term perspective on deforestation rates in the Brazilian Amazon

2015 ◽  
Vol XL-7/W3 ◽  
pp. 539-544 ◽  
Author(s):  
M. D. Velasco Gomez ◽  
R. Beuchle ◽  
Y. Shimabukuro ◽  
R. Grecchi ◽  
D. Simonetti ◽  
...  
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Brazilian Amazon ◽  
Thematic Mapper ◽  
Tree Cover ◽  
Amazon Forest ◽  
Forest Cover Change ◽  
Visual Interpretation ◽  
Deforestation Rates ◽  
Arc Of Deforestation

Monitoring tropical forest cover is central to biodiversity preservation, terrestrial carbon stocks, essential ecosystem and climate functions, and ultimately, sustainable economic development. The Amazon forest is the Earth’s largest rainforest, and despite intensive studies on current deforestation rates, relatively little is known as to how these compare to historic (pre 1985) deforestation rates. We quantified land cover change between 1975 and 2014 in the so-called Arc of Deforestation of the Brazilian Amazon, covering the southern stretch of the Amazon forest and part of the Cerrado biome. We applied a consistent method that made use of data from Landsat sensors: Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). We acquired suitable images from the US Geological Survey (USGS) for five epochs: 1975, 1990, 2000, 2010, and 2014. We then performed land cover analysis for each epoch using a systematic sample of 156 sites, each one covering 10 km × 10 km, located at the confluence point of integer degree latitudes and longitudes. An object-based classification of the images was performed with five land cover classes: tree cover, tree cover mosaic, other wooded land, other land cover, and water. The automatic classification results were corrected by visual interpretation, and, when available, by comparison with higher resolution imagery. Our results show a decrease of forest cover of 24.2% in the last 40 years in the Brazilian Arc of Deforestation, with an average yearly net forest cover change rate of -0.71% for the 39 years considered.

scholarly journals Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

2019 ◽  
Vol 11 (19) ◽  
pp. 2286
Author(s):  
Libo Wang ◽  
Paul Bartlett ◽  
Darren Pouliot ◽  
Ed Chan ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
Forest Cover ◽  
Reference Sample ◽  
Tree Cover ◽  
Initial Surface ◽  
Forest Cover Change ◽  
Global Land Cover ◽  
Global Land

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

scholarly journals Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia

2015 ◽  
Vol XL-7/W3 ◽  
pp. 557-562 ◽  
Author(s):  
A. Wijaya ◽  
R. A. Sugardiman Budiharto ◽  
A. Tosiani ◽  
D. Murdiyarso ◽  
L.V. Verchot
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Information ◽  
Brazilian Amazon ◽  
Congo Basin ◽  
Reliable Estimate ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change

Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

Amazon forest cover change mapping based on semantic segmentation by U-Nets

2021 ◽  
Vol 62 ◽  
pp. 101279
Author(s):  
L. Bragagnolo ◽  
R.V. da Silva ◽  
J.M.V. Grzybowski
Keyword(s):  
Forest Cover ◽  
Semantic Segmentation ◽  
Amazon Forest ◽  
Forest Cover Change

scholarly journals Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 173
Author(s):  
Changjun Gu ◽  
Yili Zhang ◽  
Linshan Liu ◽  
Lanhui Li ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Ecosystem Service ◽  
Forest Cover ◽  
Google Earth ◽  
Forest Cover Change ◽  
Sacred Landscape ◽  
Lulc Changes ◽  
Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

The forest resource of Labrador: Landsat Thematic Mapper imagery provides a unique and current perspective

1993 ◽  
Vol 69 (6) ◽  
pp. 667-671 ◽  
Author(s):  
John A. Drieman
Keyword(s):  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Resource ◽  
Visual Interpretation ◽  
Digital Format ◽  
Current Perspective ◽  
Colour Composite ◽  
Verification Process ◽  
Forest Cover Types

The need for a current, regional perspective of the forest of Labrador was identified. Mapping of forest cover types, peat-lands, recent burns and clearcut disturbances was accomplished through visual interpretation of 1:1,000,000 scale Landsat Thematic mapper colour composite transparencies and the transfer of interpreted polygons to a geographic information system. The mapping and verification process is described in this paper. The end product, a forest resource map, provides the most up-to-date and detailed information on Labrador's forest cover types and disturbances available on a single map. The digital format of the map facilities area summaries, viewing and printing.

scholarly journals ALTERAÇÃO NA COBERTURA FLORESTAL DA REGIÃO NOROESTE DA AMAZÔNIA MATOGROSSENSE

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 520
Author(s):  
Luani Rosa de Oliveira Piva ◽  
Rorai Pereira Martins Neto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Forest Conversion ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Landsat 8 Oli ◽  
Mato Grosso ◽  
The Past ◽  
Landsat 5 Tm

Nos últimos anos, a intensificação das atividades antrópicas modificadoras da cobertura vegetal do solo em território brasileiro vem ocorrendo em larga escala. Para fins de monitoramento das alterações da cobertura florestal, as técnicas de Sensoriamento Remoto da vegetação são ferramentas imprescindíveis, principalmente em áreas extensas e de difícil acesso, como é o caso da Amazônia brasileira. Neste sentido, objetivou-se com este trabalho identificar as mudanças no uso e cobertura do solo no período de 20 anos nos municípios de Aripuanã e Rondolândia, Noroeste do Mato Grosso, visando quantificar as áreas efetivas que sofreram alterações. Para tal, foram utilizadas técnicas de classificação digital de imagens Landsat 5 TM e Landsat 8 OLI em três diferentes datas (1995, 2005 e 2015) e, posteriormente, realizada a detecção de mudanças para o uso e cobertura do solo. A classificação digital apresentou resultados excelentes, com índice Kappa acima de 0,80 para os mapas gerados, indicando ser uma ferramenta potencial para o uso e cobertura do solo. Os resultados denotaram uma conversão de áreas florestais principalmente para atividades antrópicas agrícolas, na ordem de 472 km², o que representa uma perda de 1,3% de superfície de floresta amazônica na região de estudo.Palavras-chave: conversão de áreas florestais; uso e cobertura do solo; classificação digital; análise multitemporal. CHANGE IN FOREST COVER OF THE NORTHWEST REGION OF AMAZON IN MATO GROSSO STATE ABSTRACT: In the past few years, the intensification of anthropic activities that modify the soil-vegetation cover in Brazil’s land has been occurring on a large scale. To monitor the forest cover changes, the techniques of Remote Sensing of vegetation are essential tools, especially in large areas and with difficult access, as is the case of the Brazilian Amazon. The aim of this work was to identify the changes in land use and land cover, over the past 20 years, in the municipalities of Aripuanã and Rondolândia, Northwest of Mato Grosso State, in order to quantify the effective altered areas. Landsat 5 TM and Landsat 8 OLI digital classification images techniques were used in three different dates (1995, 2005 and 2015) and, later, the detection to the land use and land cover changes. The digital classification showed excellent results, with kappa index above 0.80 for the generated maps, indicating the digital classification as a potential tool for land use and land cover. Results reflect the conversion of forest areas mainly for agricultural activities, in the order of 472 km², representing a loss of 1.3% of Amazon forest surface in the study region.Keywords: forest conversion; land use and land cover; digital classification; multitemporal analysis.

Forest cover change patterns in Myanmar (Burma) 1990–2000

2005 ◽  
Vol 32 (4) ◽  
pp. 356-364 ◽  
Author(s):  
PETER LEIMGRUBER ◽  
DANIEL S. KELLY ◽  
MARC K. STEININGER ◽  
JAKE BRUNNER ◽  
THOMAS MÜLLER ◽  
...  
Keyword(s):  
Forest Cover ◽  
Mangrove Forests ◽  
Forest Cover Change ◽  
Charcoal Production ◽  
Important Species ◽  
Agricultural Conversion ◽  
Deforestation Rates ◽  
Commercial Logging ◽  
Closed Canopy ◽  
Landsat Satellite

Myanmar is one of the most forested countries in mainland South-east Asia. These forests support a large number of important species and endemics and have great value for global efforts in biodiversity conservation. Landsat satellite imagery from the 1990s and 2000s was used to develop a countrywide forest map and estimate deforestation. The country has retained much of its forest cover, but forests have declined by 0.3% annually. Deforestation varied considerably among administrative units, with central and more populated states and divisions showing the highest losses. Ten deforestation hotspots had annual deforestation rates well above the countrywide average. Major reasons for forest losses in these hotspots stemmed from increased agricultural conversion, fuelwood consumption, charcoal production, commercial logging and plantation development. While Myanmar continues to be a stronghold for closed canopy forests, several areas have been experiencing serious deforestation. Most notable are the mangrove forests in the Ayeyarwady delta region and the remaining dry forests at the northern edge of the central dry zone.

scholarly journals Chironomidae from Eastern Amazon: Understanding the differences of land-use on functional feeding groups

2018 ◽  
Author(s):  
Kathia C. Sonoda ◽  
Josinete S. Monteles ◽  
Anderson Ferreira ◽  
Pedro Gerhard
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Development ◽  
Brazilian Amazon ◽  
Human Action ◽  
Taxonomic Resolution ◽  
Functional Feeding Groups ◽  
Amazon Forest ◽  
Group Data ◽  
Forest Streams

Deforestation for agricultural purposes is the most dangerous human action against the conservation of the Brazilian Amazon Forest; its rates reached almost 20% of the original forested area. Many studies have been conducted on Chironomidae systematics and ecology over the Amazon biome, but most concerned the Central Amazon, while little is known about Chironomidae diversity and the effects of land development and agriculture intensification on the aquatic biota from Eastern Brazilian Amazon. The present study analyzed the effects of different land-use and land-cover on Chironomidae assemblages. Land-Use and Land-Cover (LULC) at the riparian zone were assessed from satellite imagery and three categories were defined: Forest, Secondary (Capoeira) and Agriculture. Ten catchments were selected: two for Forest, five for Agriculture and three for Secondary. For each catchment we characterized habitat and sampled insects. We hypothesized that i) the assemblage taxonomic richness will change across different land uses on riparian zones and ii) feeding functionality is a better information than taxonomic resolution to show the importance of LULC upon stream. A total of 20,884 individuals were sampled from the streams, abundance was higher in Agriculture streams. Corynoneura (18.4%), Pentaneura (14.6%) and Rheotanytarus (14.0%) were the most abundant genera in Agriculture streams; Corynoneura (17.8%), Caladomyia (13.6%), Paratanytarsus (13.1%) and Beardius (10.9%) dominated Forest streams; Goeldichironomus (25.9%), Rheotanytarus (17.6%) and Polypedilum (13.2%) dominated Capoeira streams. Regarding FFG, gatherers were the most numeric abundant in Forest (50.3%), followed by filterers (38.7%), predators (6.6%) and shredders (4.2%). In Capoeira, filterers were the main FFG (61.1%), gatherers (27.9%), predators (7.7%) and shredders (3.3%). In Agriculture streams, predators, filterers and gatherers had close numeric participation, 34.9%, 32.4% and 32.2%, respectively. Shredders performed a smaller fraction (0.4%). In Forest and Agriculture, scrapers participation was under 0.2%, while it was absent at Capoeira. Permutation tests showed significant differences among assemblages, based on numerical abundance of genera and on functional feeding group data. Even though, shredders showed a discrete participation in all three LULC, it was statistically significant higher at Forest streams when compared to Agriculture ones. Our study was able to demonstrate taxonomic differences of all LULC analyzed and it also showed the importance in considering the feeding behavior to understand the effects of land-use and land-covers changes.

scholarly journals Tree Cover Estimation in Global Drylands from Space Using Deep Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 343 ◽  
Author(s):  
Emilio Guirado ◽  
Domingo Alcaraz-Segura ◽  
Javier Cabello ◽  
Sergio Puertas-Ruíz ◽  
Francisco Herrera ◽  
...  
Keyword(s):  
Forest Cover ◽  
Global Scale ◽  
Classification Model ◽  
Tree Cover ◽  
System Modelling ◽  
Earth System ◽  
Visual Interpretation ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
The Cost

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management.

scholarly journals Multitemporal Analysis Using Landsat Thematic Mapper (TM) Bands for Forest Cover Classification in East Texas

2008 ◽  
Vol 32 (1) ◽  
pp. 21-27
Author(s):  
Jason C. Raines ◽  
Jason Grogan ◽  
I-Kuai Hung ◽  
James Kroll
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Types ◽  
Significant Difference ◽  
Potential Benefits ◽  
Land Cover Maps ◽  
Band Combinations

Abstract Land cover maps have been produced using satellite imagery to monitor forest resources since the launch of Landsat 1. Research has shown that stacking leaf-on and leaf-off imagery (combining two separate images into one image for processing) may improve classification accuracy. It is assumed that the combination of data will aid in differentiation between forest types. In this study we explored potential benefits of using multidate imagery versus single-date imagery for operational forest cover classification as part of an annual remote sensing forest inventory system. Landsat Thematic Mapper (TM) imagery was used to classify land cover into four classes. Six band combinations were tested to determine differences in classification accuracy and if any were significant enough to justify the extra cost and increased difficulty of image acquisition. The effects of inclusion/exclusion of the moisture band (TM band 5) also were examined. Results show overall accuracy ranged from 72 to 79% with no significant difference between single and multidate classifications. We feel the minimal increase (3.06%) in overall accuracy, coupled with the operational difficulties of obtaining multiple (two), useable images per year, does not support the use of multidate stacked imagery. Additional research should focus on fully utilizing data from a single scene by improving classification methodologies.

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