Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets

The Ecological Footprint methodology is a framework that tracks Ecological Footprint (humanity’s demands on the biosphere) by comparing human demand against the regenerative capacity (Biocapacity) of the planet (WWF, 2010) to advance the science of sustainability. As such, the spatiotemporal dynamics of the Ecological Footprint (EF) and Biocapacity (BC) in a given watershed are important topics in the field of sustainability research based on remote sensing (RS) data and geographic information system (GIS) techniques．This paper reports on a case study of the Jinghe River Watershed using improved EF methodology with the help of GIS and high resolution remote sensing data, to quantitatively estimate the relationship between EF demand and BC supply and analyze their spatial distribution patterns at multiple spatial scales for four periods (1986, 1995, 2000 and 2008). We predict the future BC both overall, and of six categories of biological productivity area for the next four decades using the Markov Chain Method．The results showed that the spatial distribution of EF demand and BC supply were significantly uneven in the region, in which the per-capita EF of all counties located in the watershed increased continually from 1986 to 2008, and the EF per person of counties in the middle and lower reaches area was markedly greater than that in the upper reaches over time. On the supply side, the per-capita BC of all counties decreased gradually from 1986 to 2008, and the per-capita BC of counties in the upper reaches area was greater than that in the middle and lower reaches during the period, causing the uneven spatial distribution of Ecological budget-the gap between supply and demand, showed that the Jinghe River Watershed on the whole has begun to be unsustainable since 2008, with each county exhibiting differential temporal patterns. The prediction results showed that the total BC will increase continually from 2020 to 2050, and the BC of six categories will reduce, indicating that unsustainability in the region will escalate. As a whole, The EF demand has exceeded the BC supply, and the gap was widening in the Jinghe Watershed. This paper provided an in-depth portrait of the spatiotemporal dynamics of EF and BC, as well as their interactions with humanity and ecosystems.

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Modeling Long-term Forest Carbon Spatiotemporal Dynamics With Historical Climate and Recent Remote Sensing Data

Science Foundation in China ◽

10.1088/1005-0841/18/1/004 ◽

2010 ◽

Vol 18 (1) ◽

pp. 38-79

Author(s):

Chen Jing

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Spatiotemporal Dynamics ◽

Forest Carbon ◽

Sensing Data ◽

Historical Climate

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Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

Remote Sensing ◽

10.3390/rs12010055 ◽

2019 ◽

Vol 12 (1) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Cong Ou ◽

Jianyu Yang ◽

Zhenrong Du ◽

Yiming Liu ◽

Quanlong Feng ◽

...

Keyword(s):

Remote Sensing ◽

Landsat Imagery ◽

Google Earth ◽

Spatiotemporal Dynamics ◽

Landscape Patterns ◽

Agricultural Region ◽

Multi Temporal ◽

Google Earth Engine ◽

Production Approach

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

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