Satellite-based assessment of large-scale land cover change in Asian arid regions in the period of 2001–2009

2013 ◽  
Vol 71 (9) ◽  
pp. 3935-3944 ◽  
Author(s):  
Jaeil Cho ◽  
Yang-Won Lee ◽  
Pat J.-F. Yeh ◽  
Kyung-Soo Han ◽  
Shinjiro Kanae
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Arid Regions ◽  
Large Scale ◽  
Large Scale Land

scholarly journals Biogeophysical versus biogeochemical feedbacks of large-scale land cover change

2001 ◽  
Vol 28 (6) ◽  
pp. 1011-1014 ◽  
Author(s):  
Martin Claussen ◽  
Victor Brovkin ◽  
Andrey Ganopolski
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Large Scale ◽  
Large Scale Land

scholarly journals Domain Adversarial Neural Networks for Large-Scale Land Cover Classification

2019 ◽  
Vol 11 (10) ◽  
pp. 1153 ◽  
Author(s):  
Mesay Belete Bejiga ◽  
Farid Melgani ◽  
Pietro Beraldini
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Domain Adaptation ◽  
Land Cover Classification ◽  
Target Domain ◽  
Training Samples ◽  
Latent Space ◽  
Large Scale Land

Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%.

scholarly journals Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 382
Author(s):  
Carson Baughman ◽  
Rachel Loehman ◽  
Dawn Magness ◽  
Lisa Saperstein ◽  
Rosemary Sherriff
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Large Scale ◽  
Forest Canopy ◽  
Tree Cover ◽  
Forest Loss ◽  
Environmental Response ◽  
Kenai Peninsula ◽  
Vegetation Shifts ◽  
Forest Land Cover

Across Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward given that reduction in tree cover is a readily detectable and measurable land-cover change. Land-cover change is more difficult to quantify when disturbance events are unknown, remote, or environmental response is slow in relation to human observation. While disturbance events and related land-cover change are relatively instant, assessing patterns of post-disturbance succession requires long term monitoring. Here, we describe a method for classifying land cover and quantifying land-cover change over time, using Landsat legacy imagery for three historical eras on the western Kenai Peninsula: 1973–2002, 2002–2017, and 1973–2017. Scenes from numerous Landsat sensors, including summer and winter seasons, were acquired between 1973 and 2017 and used to classify vegetation cover using a random forest classifier. Land-cover type was summarized by era and combined to produce a dataset capturing spatially explicit land-cover change at a moderate 30-m resolution. Our results document large-scale forest loss across the study area that can be attributed to known disturbance events including beetle kill and wildfire. Despite numerous and extensive disturbances resulting in forest loss, we estimate that the study area has experienced net forest gain over the duration of our study period due to reforestation within large fire events that predate this study. Transition between forest and graminoid non-forest land cover including wetlands and herbaceous uplands is the most common land-cover change—representing recruitment of a graminoid dominated understory following forest loss and the return of forest canopy given sufficient time post-disturbance.

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