scholarly journals Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000–2020 Using the Google Earth Engine

2021 ◽  
Vol 13 (24) ◽  
pp. 5134
Author(s):  
Junzhi Ye ◽  
Yunfeng Hu ◽  
Lin Zhen ◽  
Hao Wang ◽  
Yuxin Zhang
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Surface ◽  
Socioeconomic Development ◽  
Google Earth ◽  
Classification Algorithm ◽  
Driving Mechanism ◽  
Long Time Series ◽  
Long Time ◽  
Google Earth Engine

Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm was applied to create a yearly land-use/land-cover change (LULC) dataset in Xilingol during the past 20 years (2000–2020) and to examine the spatiotemporal characteristics, dynamic changes, and driving mechanisms of LULC using principal component analysis and multiple linear stepwise regression methods. The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). Cropland increases first and then decreases (−34.85%) and is mainly distributed in the southeast. The area of deserted land decreases in the south and increases in the center and north, but the total area still decreases (−13.74%). The built-up land expands rapidly (+108.45%). (3) In addition, our results suggest that regional socioeconomic development factors are the primary causes of changes in built-up land, and climate-related factors are the primary causes of water changes, but the correlations between other land-use types and relevant factors are not significant (cropland and grassland). We conclude that the GEE+RF method is capable of automated, long time-series, and high-accuracy land-use mapping, and further changes in climatic, environmental, and socioeconomic development factors, i.e., climate warming and rotational grazing, might have significant implications on regional land surface morphology and landscape dynamics.

scholarly journals Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

2021 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
Dong Chen ◽  
Yafei Wang ◽  
Zhenyu Shen ◽  
Jinfeng Liao ◽  
Jiezhi Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Change Detection ◽  
Coastal Zone ◽  
Google Earth ◽  
Coastal Zones ◽  
Long Time Series ◽  
Coastal Aquaculture ◽  
Long Time

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

scholarly journals Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

2020 ◽  
Vol 12 (19) ◽  
pp. 3202
Author(s):  
Xinran Chen ◽  
Yulin Zhan ◽  
Yan Liu ◽  
Xingfa Gu ◽  
Tao Yu ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Temperature Index ◽  
Long Time Series ◽  
Ndvi Time Series ◽  
Long Time

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

2016 ◽  
Vol 54 (9) ◽  
pp. 5301-5318 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Yang Xiang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Surface Reflectance ◽  
Area Index ◽  
Long Time Series ◽  
Global Land ◽  
Long Time

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

2019 ◽  
Vol 32 (21) ◽  
pp. 7521-7537 ◽  
Author(s):  
Daniel Fiifi Tawia Hagan ◽  
Guojie Wang ◽  
X. San Liang ◽  
Han A. J. Dolman
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Information Flow ◽  
Air Temperature ◽  
Land Surface ◽  
Full Range ◽  
Accurate Estimation ◽  
Time Varying ◽  
Long Time Series ◽  
Long Time

Abstract The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

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 ANÁLISE DO USO DA TERRA EM UMA SECÇÃO ÀS MARGENS DO RIO TOCANTINS AUXILIADA POR ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA – NDVI

2019 ◽  
Vol 5 (18) ◽  
pp. 9379
Author(s):  
Rodrigo Lima Santos ◽  
Fabrizia Gioppo Nunes
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Google Earth ◽  
Fish Ponds ◽  
Google Earth Engine ◽  
Tocantins River ◽  
Para Determinar ◽  
Del Rio

LAND USE ANALISYS IN A SECTION OF TOCANTINS’S RIVER MARGINAL STRIP SUPPORTED BY NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)ANÁLISIS DEL USO DE LA TIERRA EN UNA SECCIÓN LOS MARGENES DEL RÍO TOCANTINS AUXILIADOS POR ÍNDICE DE VEGETACIÓN POR DIFERENCIA NORMALIZADA – NDVIRESUMOO mapeamento de uso e cobertura da terra é um instrumento indispensável para uma boa gestão do ambiente em geral, podendo obedecer diferentes recortes espaciais. A adoção de alternativas para aperfeiçoar esse produto, tais como a leitura de dados anuais de Índices de Vegetação torna-o mais efetivo e capaz de oferecer respostas a determinadas questões. Nesta perspectiva, o presente estudo tem como objetivo o mapeamento e reconhecimento de áreas degradadas e de áreas preservadas, em uma secção delimitada as margens do Rio Tocantins, auxiliados por séries temporais de NDVI. A metodologia incluiu o mapeamento de uso e cobertura da terra no ano de 2015; delimitação da Área de Proteção Ambiental (APP) e; a utilização de séries anuais de NDVI, disponibilizadas pela plataforma online do Google Earth Engine. A ferramenta de NDVI é apresentada como uma alternativa a avaliação da conversão de coberturas naturais para diferentes tipologias de uso da terra. Como exemplos, são retratados três pontos de conversões de uso: solo exposto para vegetação regenerada; vegetação natural para tanques de pisciculturas e; solo exposto intercalado à vegetação rasteira para área urbanizada. Os resultados apontam que a APP analisada se encontra em estado de alerta, uma vez que sua conversão em áreas degradadas ultrapassa cerca de 50%, e a ferramenta de NDVI foi essencial para determinar quando ocorreram essas modificações em distintas classes de uso.Palavras-chave: Uso da Terra; Séries Temporais; Rio Tocantins; Imperatriz-MA.ABSTRACTThe mapping of land use and land cover is an indispensable tool for good management of the environment in general, and can obey different spatial cutouts. Adopting alternatives to improve this product, such as reading annual Vegetation Index data makes it more effective and able to provide answers to certain questions. In this perspective, the present study aims to map and recognize degraded areas and preserved areas, in a section delimited the banks of the Tocantins River, aided by NDVI time series. The methodology included land use and land cover mapping in 2015; delimitation of the Environmental Protection Area (APP) and; use of annual NDVI series made available through the Google Earth Engine online platform. The NDVI tool is presented as an alternative to evaluate the conversion of natural coverages for different land use typologies. As examples, three points of use conversions are depicted: exposed soil for regenerated vegetation; natural vegetation for fish ponds and; exposed soil interspersed with undergrowth to urbanized area. The results indicate that the analyzed APP is in a state of alert, since its conversion to degraded areas exceeds about 50%, and the NDVI tool was essential to determine when these changes occurred in different classes of use.Keywords: Land Use; Time Series; Tocantins River; Imperatriz-MA.RESUMENEl mapeo del uso de la tierra y la cobertura de la tierra es una herramienta indispensable para la buena gestión del medio ambiente en general, y puede obedecer a diferentes recortes espaciales. Adoptar alternativas para mejorar este producto, como leer los datos anuales del Índice de Vegetación, lo hace más efectivo y capaz de proporcionar respuestas a ciertas preguntas. En esta perspectiva, este estudio apunta a mapear y reconocer áreas degradadas y áreas preservadas, en una sección delimitada a orillas del río Tocantins, ayudado por series de tiempo NDVI. La metodología incluyó el uso del suelo y el mapeo de la cobertura del suelo en 2015; delimitación del Área de Protección Ambiental (APP) y; uso de la serie anual NDVI disponible a través de la plataforma en línea Google Earth Engine. La herramienta NDVI se presenta como una alternativa para evaluar la conversión de coberturas naturales para diferentes tipologías de uso de la tierra. Como ejemplos, se representan tres conversiones de puntos de uso: suelo expuesto para vegetación regenerada; vegetación natural para estanques de peces y; suelo expuesto intercalado con maleza en el área urbanizada. Los resultados indican que la aplicación analizada se encuentra en estado de alerta, ya que su conversión a áreas degradadas supera aproximadamente el 50%, y la herramienta NDVI fue esencial para determinar cuándo ocurrieron estos cambios en diferentes clases de uso.Palabras clave: Uso de la Tierra; Series Temporales; Río Tocantins; Imperatriz-MA.

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