Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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RS & GIS-Based Spatialtemporal Analysis of Ecological Footprint and Biocapacity Pattern of Jinghe River Watershed in China: Does Supply Meet Demand?

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.356-360.2820 ◽

2011 ◽

Vol 356-360 ◽

pp. 2820-2832

Author(s):

Dong Xia Yue ◽

Jin Hui Ma ◽

Jian Jun Guo ◽

Jia Jing Zhang ◽

Jun Du ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Ecological Footprint ◽

Spatial Scales ◽

Supply And Demand ◽

Remote Sensing Data ◽

Spatiotemporal Dynamics ◽

River Watershed ◽

Per Capita ◽

Jinghe River

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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Aerosol Optical Properties over South Asia from Ground-Based Observations and Remote Sensing: A Review

Climate ◽

10.3390/cli1030084 ◽

2013 ◽

Vol 1 (3) ◽

pp. 84-119 ◽

Cited By ~ 21

Author(s):

S. Ramachandran ◽

Sumita Kedia

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

South Asia ◽

Aerosol Optical Properties

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Geostatistics of Active Fire Remote Sensing Data - Examples from South Asia

10.21203/rs.3.rs-243446/v1 ◽

2021 ◽

Author(s):

Jyoti U. Devkota

Keyword(s):

Remote Sensing ◽

South Asia ◽

Spatial Statistics ◽

Geographical Location ◽

Remote Sensing Data ◽

Spatial Behavior ◽

Spatial Error ◽

Spatial Lag Model ◽

Active Fire ◽

Lag Model

Abstract Active fires illuminated on the earth surface are caught by the satellite. These fires are created by various sources such as vegetation fires, gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Near real time active fire data is collected using remote sensing techniques of satellites. Amount of active fires in an area is a proxy indicator of aerosols, green houses gases and trace gases. Here the behavior of active fires over a period of one year in Nepal, Bhutan and Srilanka are studied using spatial statistics. This study is based on data acquired through remote sensing of data acquisition platform, NASA’s MODIS. Spatial statistics is used here to study the incidence of active fires with respect to geographical location. The behavior of parameters of various autoregressive models like Spatial Durban Model, Spatial Lag Model, Spatial Error Model, Manski Model and Kelegian Prucha Model are minutely analyzed. The best model with highest pseudo R2 is selected. The spatial behavior of the fire radiative power for three countries is also predicted using spatial interpolation and kriging. So the burning potential of vegetations in unsampled areas is envisaged by thus predicting FRP. Such studies give a country wise perspective to the behavior of fire; this is with reference to south Asia. They are of great importance for countries of developing world which lack a strong backbone of good quality official records. Through the statistical analyses of data collected by such platforms, important information can be indirectly assessed.

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A Quantitative Inspection on Spatio-Temporal Variation of Remote Sensing-Based Estimates of Land Surface Evapotranspiration in South Asia

Remote Sensing ◽

10.3390/rs70404726 ◽

2015 ◽

Vol 7 (4) ◽

pp. 4726-4752 ◽

Cited By ~ 9

Author(s):

Ainong Li ◽

Wei Zhao ◽

Wei Deng

Keyword(s):

Remote Sensing ◽

Temporal Variation ◽

South Asia ◽

Land Surface ◽

Spatio Temporal

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

Agricultural Water Management ◽

10.1016/j.agwat.2022.107458 ◽

2022 ◽

Vol 263 ◽

pp. 107458

Author(s):

Chao Zhang ◽

Jinwei Dong ◽

Lijun Zuo ◽

Quansheng Ge

Keyword(s):

Remote Sensing ◽

Spatiotemporal Dynamics

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Monitoring reservoir storage in South Asia from multisatellite remote sensing

Water Resources Research ◽

10.1002/2014wr015829 ◽

2014 ◽

Vol 50 (11) ◽

pp. 8927-8943 ◽

Cited By ~ 55

Author(s):

Shuai Zhang ◽

Huilin Gao ◽

Bibi S. Naz

Keyword(s):

Remote Sensing ◽

South Asia ◽

Reservoir Storage

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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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