scholarly journals Land Use Land Classification (LULC) Change Detection with High Cadence Multimodal Image Time Series and Self-Supervised Learning.

2021 ◽  
Author(s):  
Akhil Singh Rana ◽  
Caglar Senaras ◽  
Benjamin Bischke ◽  
Patrick Helber ◽  
Timothy Davis ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Change Detection ◽  
Supervised Learning ◽  
Land Classification ◽  
Lulc Change ◽  
High Cadence

scholarly journals Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia

2018 ◽  
Vol 11 ◽  
pp. 117862211775160 ◽  
Author(s):  
Gebiaw T Ayele ◽  
Aschalew K Tebeje ◽  
Solomon S Demissie ◽  
Mulugeta A Belete ◽  
Mengistu A Jemberrie ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Population Growth ◽  
Change Detection ◽  
Land Cover Change ◽  
Training Data ◽  
Steady Increase ◽  
Northern Ethiopia ◽  
Detection Techniques

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

scholarly journals DETECÇÃO DE ÁREAS DE FLORESTAS INVARIANTES EM SÉRIES TEMPORAIS UTILIZANDO RANDOM FOREST

GEOgraphia ◽  
2021 ◽  
Vol 23 (50) ◽  
Author(s):  
Eduardo Ribeiro Lacerda ◽  
Raúl Sanchéz Vicens
Keyword(s):  
Land Use ◽  
Time Series ◽  
Random Forest ◽  
Change Detection ◽  
Vegetation Change ◽  
Google Earth ◽  
Detection Algorithms ◽  
Palabras Clave ◽  
Change Detection Algorithms ◽  
Google Earth Engine

O surgimento de algoritmos de detecção de mudanças na vegetação na última década é impressionante. Mas os resultados gerados ainda possuem ruído que precisa ser tratado com a utilização de resultados de outros mapeamentos de cobertura vegetal. Além disso, a necessidade de gerar classes de uso do solo invariantes é importante para o melhor entendimento de processos que ocorrem em áreas florestais. Pensando nisso, este trabalho busca criar uma nova forma de mapear essas áreas invariáveis que possam ser utilizadas para mascarar ruídos e também como subsídio para outros estudos de conservação e restauração. A metodologia proposta aqui usa a plataforma Google Earth Engine e um algoritmo de aprendizado de máquina: o Random Forest, para classificar áreas de floresta invariáveis usando todo o acervo de imagens da série temporal Landsat, de uma só vez. Os resultados mostraram que a nova abordagem teve melhor desempenho do que o uso de técnicas mais tradicionais como a agregação de mapeamentos de uso e cobertura anuais, com uma acurácia global de 91,7%. O trabalho busca ainda contribuir com a comunidade de sensoriamento remoto ao apresentar, após exaustivos testes, as melhores opções de variáveis a serem utilizadas neste tipo de classificação. Palavras-chave: Séries Temporais, Detecção de Mudanças, Florestas, Google Earth Engine, Random Forest.DETECTION OF INVARIANT VEGETATION AREAS IN TIME SERIES USING RANDOM FOREST ALGORITHMAbstract: The emergence of vegetation change detection algorithms in the last decade is impressive. But the results still have a lot of noise that needs to be cleaned. And the data cleaning process still uses other landcover mapping results. Besides that, the necessity to generate invariant land use classes is important to know particularly to forest areas. Thinking about that, this paper seeks to create a new form of mapping these invariant areas that can be used to mask noise and as an input on other conservation and restoration studies. The methodology proposed here uses the Google Earth Engine platform and a Random Forest algorithm to classify invariant forest areas using all the image’s collection in the time series at once. The results showed that the new approach performed better than the use of more traditional techniques such as the aggregation of annual land-use and land-cover mappings, with an overall accuracy of 91.7%. Also, this paper seeks to contribute to the remote sensing community showing after exhaustive testing, good options of variables to use on this type of work. Keywords: Time Series, Change Detection, Forests, Google Earth Engine, Random Forest.DETECCIÓN DE ÁREAS DE VEGETACIÓN INVARIANTES EN SÉRIES TEMPORALES UTILIZANDO ALGORITMO RANDOM FORESTResumen: La aparición de algoritmos de detección de cambios en la vegetación en la última década es impresionante. Pero los resultados todavía tienen muchos ruidos que deben ser eliminados. Además, el proceso de limpieza de datos se basa en otros mapas de cobertura de la tierra. Además de eso, es importante conocer la necesidad de generar clases de uso de la tierra invariables, particularmente en las áreas forestales. Pensando en eso, este artículo busca crear una nueva forma de mapear estas áreas invariantes que se pueden utilizar para enmascarar el ruido y como un aporte para otros estudios de conservación y restauración. La metodología propuesta aquí utiliza la plataforma Google Earth Engine y un algoritmo de aprendizaje de máquina: o Random Forest para clasificar áreas invariantes de bosque, utilizando a la vez todas las imágenes de la serie temporal Landsat. Los resultados encontraron que el nuevo enfoque tuvo mejor desempeño que el uso de técnicas tradicionales, con una precisión global del 91,7%. Este trabajo busca además contribuir con la comunidad de la teledetección, mostrando mediante de exhaustivas pruebas, mejores opciones de variables para utilizar en este tipo de clasificación. Palabras clave: Series de Tiempo, Detección de Cambios, Bosques, Google Earth Engine, Random Forest.

scholarly journals Land-Use and Land-Cover (LULC) Change Detection in Wami River Basin, Tanzania

Land ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 136 ◽  
Author(s):  
Sekela Twisa ◽  
Manfred F. Buchroithner
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Population Growth ◽  
Change Detection ◽  
River Basin ◽  
Land Use Planning ◽  
Management Practice ◽  
Cultivated Land ◽  
Lulc Change ◽  
Lulc Changes

Anthropogenic activities have substantially changed natural landscapes, especially in regions which are extremely affected by population growth and climate change such as East African countries. Understanding the patterns of land-use and land-cover (LULC) change is important for efficient environmental management, including effective water management practice. Using remote sensing techniques and geographic information systems (GIS), this study focused on changes in LULC patterns of the upstream and downstream Wami River Basin over 16 years. Multitemporal satellite imagery of the Landsat series was used to map LULC changes and was divided into three stages (2000–2006, 2006–2011, and 2011–2016). The results for the change-detection analysis and the change matrix table from 2000 to 2016 show the extent of LULC changes occurring in different LULC classes, while most of the grassland, bushland, and woodland were intensively changed to cultivated land both upstream and downstream. These changes indicate that the increase of cultivated land was the result of population growth, especially downstream, while the primary socioeconomic activity remains agriculture both upstream and downstream. In general, net gain and net loss were observed downstream, which indicate that it was more affected compared to upstream. Hence, proper management of the basin, including land use planning, is required to avoid resources-use conflict between upstream and downstream users.

RapidAI4EO: Advancing the State-of-the-Art in Continuous Land Monitoring

2021 ◽  
Author(s):  
Giovanni Marchisio ◽  
Patrick Helber ◽  
Benjamin Bischke ◽  
Tim Davis ◽  
Annett Wania
Keyword(s):  
Land Use ◽  
European Union ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Training Data ◽  
Third Party ◽  
The European Union ◽  
Horizon 2020 ◽  
High Cadence

<p>New catalogues of nearly daily or even intraday temporal data will soon dominate the global archives. However, there has been little exploration of artificial intelligence (AI) techniques to leverage the high cadence that is already possible to achieve through the fusion of multiscale, multimodal sensors. Under the sponsorship of the European Union’s Horizon 2020 programme, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. Focus is on the CORINE Land Cover programme, which is the flagship of CLMS. </p><p>Specific objectives of the project are to: 1) explore and stimulate the development of new spatiotemporal monitoring applications based on the latest advances in AI and Deep Learning (DL); 2) demonstrate the fusion of Copernicus high resolution satellite imagery and third party very high resolution imagery; 3) provide intensified monitoring of Land Use and Land Cover, and Land Use change at a much higher level of detail and temporal cadence than it is possible today. </p><p>Our strategy is two-fold. The first aspect involves developing vastly improved DL architectures to model the phenomenology inherent in high cadence observations with focus on disentangling phenology from structural change. The second involves providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we will create the most complete and dense spatiotemporal training sets ever, combining Sentinel-2 with daily, harmonized, cloud-free, gap filled, multispectral 3m time series resulting from fusion of open satellite data with Planet imagery at as many as 500,000 patch locations over Europe. The daily time series will span the entire year 2018, to coincide with the latest release of CORINE. We plan to open source these datasets for the benefit of the entire remote sensing community.</p><p>This talk focuses on the description of the datasets whose inspirations comes from the recently released EuroSAT (Helbert et al, 2019) and BigEarthNet corpora (Sumbul et al, 2019). The new corpora will look at the intersection of CORINE 2018 with all the countries in the EU, balancing relative country surface with relative LULC distribution and most notably adding the daily high resolution time series at all locations for the year 2018. Annotations will be based on the CORINE ontology. The higher spatial resolution will support modeling of more LC classes, while the added  temporal dimension should enable disambiguation of land covers across diverse climate zones, as well as an improved understanding of land use.</p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p>

scholarly journals Land-Use and Land-Cover (LULC) Change Detection and the Implications for Coastal Water Resource Management in the Wami–Ruvu Basin, Tanzania

2021 ◽  
Vol 13 (8) ◽  
pp. 4092
Author(s):  
Jamila Ngondo ◽  
Joseph Mango ◽  
Ruiqing Liu ◽  
Joel Nobert ◽  
Alfonse Dubi ◽  
...  
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Land Cover ◽  
Change Detection ◽  
Water Resource Management ◽  
Water Discharge ◽  
Bare Soil ◽  
Training Data ◽  
Lulc Change ◽  
Lulc Changes

Evaluation of river basins requires land-use and land-cover (LULC) change detection to determine hydrological and ecological conditions for sustainable use of their resources. This study assessed LULC changes over 28 years (1990–2018) in the Wami–Ruvu Basin, located in Tanzania, Africa. Six pairs of images acquired using Landsat 5 TM and 8 OLI sensors in 1990 and 2018, respectively, were mosaicked into a single composite image of the basin. A supervised classification using the Neural Network classifier and training data was used to create LULC maps for 1990 and 2018, and targeted the following eight classes of agriculture, forest, grassland, bushland, built-up, bare soil, water, and wetland. The results show that over the past three decades, water and wetland areas have decreased by 0.3%, forest areas by 15.4%, and grassland by 6.7%, while agricultural, bushland, bare soil, and the built-up areas have increased by 11.6%, 8.2%, 1.6%, and 0.8%, respectively. LULC transformations were assessed with water discharge, precipitation, and temperature, and the population from 1990 to 2018. The results revealed decreases in precipitation, water discharge by 4130 m3, temperature rise by 1 °C, and an increase in population from 5.4 to 10 million. For proper management of water-resources, we propose three strategies for water-use efficiency-techniques, a review legal frameworks, and time-based LULC monitoring. This study provides a reference for water resources sustainability for other countries with basins threatened by LULC changes.

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